From d5356061fbd3a9b3052dee25bd9c82c375c42e22 Mon Sep 17 00:00:00 2001 From: Harpreet Date: Thu, 31 Dec 2015 16:03:57 +0530 Subject: Macros example updated --- demos/lsqlin.dem.sce | 66 +++-- demos/lsqnonneg.dem.sce | 20 +- demos/qpipopt.dem.sce | 42 +-- demos/qpipoptmat.dem.sce | 26 +- demos/symphony.dem.sce | 2 +- demos/symphonymat.dem.sce | 1 + help/en_US/lsqlin.xml | 72 +++-- help/en_US/lsqnonneg.xml | 26 +- help/en_US/qpipopt.xml | 57 ++-- help/en_US/qpipoptmat.xml | 33 +-- help/en_US/scilab_en_US_help/JavaHelpSearch/DOCS | Bin 7534 -> 6899 bytes .../scilab_en_US_help/JavaHelpSearch/DOCS.TAB | Bin 872 -> 761 bytes .../en_US/scilab_en_US_help/JavaHelpSearch/OFFSETS | Bin 270 -> 264 bytes .../scilab_en_US_help/JavaHelpSearch/POSITIONS | Bin 36377 -> 33375 bytes help/en_US/scilab_en_US_help/JavaHelpSearch/SCHEMA | 2 +- help/en_US/scilab_en_US_help/JavaHelpSearch/TMAP | Bin 16384 -> 14336 bytes help/en_US/scilab_en_US_help/lsqlin.html | 72 +++-- help/en_US/scilab_en_US_help/lsqnonneg.html | 26 +- help/en_US/scilab_en_US_help/qpipopt.html | 51 ++-- help/en_US/scilab_en_US_help/qpipoptmat.html | 35 +-- help/en_US/scilab_en_US_help/symphony.html | 7 +- help/en_US/scilab_en_US_help/symphonymat.html | 6 +- help/en_US/symphony.xml | 5 +- help/en_US/symphonymat.xml | 4 +- jar/scilab_en_US_help.jar | Bin 215257 -> 205947 bytes macros/buildmacros.sce | 6 +- macros/lsqlin.bin | Bin 53548 -> 50916 bytes macros/lsqlin.sci | 84 +++--- macros/lsqnonneg.bin | Bin 23840 -> 24024 bytes macros/lsqnonneg.sci | 32 +-- macros/qpipopt.bin | Bin 50496 -> 50352 bytes macros/qpipopt.sci | 185 ++++++------- macros/qpipoptmat.bin | Bin 52464 -> 52688 bytes macros/qpipoptmat.sci | 38 +-- macros/setOptions.sci | 6 +- macros/symphony.bin | Bin 54340 -> 56296 bytes macros/symphony.sci | 30 ++- macros/symphony_call.sci | 6 +- macros/symphonymat.bin | Bin 60108 -> 60688 bytes macros/symphonymat.sci | 295 ++++++++++----------- sci_gateway/cpp/.sci_QuadNLP.cpp.swp | Bin 0 -> 16384 bytes sci_gateway/cpp/QuadNLP.hpp | 21 +- sci_gateway/cpp/builder_gateway_cpp.sce | 6 +- sci_gateway/cpp/globals.cpp | 17 +- sci_gateway/cpp/libsymphonytools.c | 144 ---------- sci_gateway/cpp/libsymphonytools.so | Bin 86003 -> 0 bytes sci_gateway/cpp/sci_QuadNLP.cpp | 17 +- sci_gateway/cpp/sci_iofunc.cpp | 14 +- sci_gateway/cpp/sci_iofunc.hpp | 13 +- sci_gateway/cpp/sci_ipopt.cpp | 18 +- .../cpp/sci_solver_status_query_functions.cpp | 16 +- sci_gateway/cpp/sci_sym_addrowcol.cpp | 16 +- sci_gateway/cpp/sci_sym_getinfinity.cpp | 16 +- sci_gateway/cpp/sci_sym_getobjsense.cpp | 16 +- sci_gateway/cpp/sci_sym_getrowact.cpp | 16 +- sci_gateway/cpp/sci_sym_isenvactive.cpp | 16 +- sci_gateway/cpp/sci_sym_load_mps.cpp | 17 +- sci_gateway/cpp/sci_sym_loadproblem.cpp | 17 +- sci_gateway/cpp/sci_sym_openclose.cpp | 16 +- sci_gateway/cpp/sci_sym_primalbound.cpp | 16 +- sci_gateway/cpp/sci_sym_remove.cpp | 17 +- sci_gateway/cpp/sci_sym_rowmod.cpp | 16 +- sci_gateway/cpp/sci_sym_set_indices.cpp | 17 +- sci_gateway/cpp/sci_sym_set_variables.cpp | 17 +- sci_gateway/cpp/sci_sym_setcolsoln.cpp | 16 +- sci_gateway/cpp/sci_sym_setobj.cpp | 15 +- sci_gateway/cpp/sci_sym_solution.cpp | 16 +- sci_gateway/cpp/sci_sym_solve.cpp | 15 +- sci_gateway/cpp/sci_sym_varbounds.cpp | 16 +- sci_gateway/cpp/sci_vartype.cpp | 16 +- sci_gateway/cpp/sym_data_query_functions.cpp | 16 +- sci_gateway/cpp/template.cpp | 43 --- tests/general_tests/lsqlin/lsqlin_A1.sce | 28 +- tests/general_tests/lsqlin/lsqlin_Aeq1.sce | 29 +- tests/general_tests/lsqlin/lsqlin_C1.sce | 26 +- tests/general_tests/lsqlin/lsqlin_b1.sce | 23 +- tests/general_tests/lsqlin/lsqlin_beq1.sce | 23 +- tests/general_tests/lsqlin/lsqlin_infeasible1.sce | 29 +- tests/general_tests/lsqlin/lsqlin_input1.sce | 27 +- tests/general_tests/lsqlin/lsqlin_input2.sce | 26 +- tests/general_tests/lsqlin/lsqlin_logical1.sce | 54 ++-- tests/general_tests/lsqlin/lsqlin_maxiter.sce | 42 +++ tests/general_tests/lsqlin/lsqlin_param1.sce | 26 +- tests/general_tests/lsqlin/lsqlin_param2.sce | 26 +- tests/general_tests/lsqlin/lsqlin_param3.sce | 26 +- tests/general_tests/lsqlin/lsqlin_x01.sce | 52 ++-- tests/general_tests/lsqlin/lsqlin_zeros.sce | 32 +++ tests/general_tests/lsqnonneg/lsqnonneg_C.sce | 20 +- tests/general_tests/lsqnonneg/lsqnonneg_input1.sce | 20 +- tests/general_tests/lsqnonneg/lsqnonneg_input2.sce | 20 +- .../general_tests/lsqnonneg/lsqnonneg_logical.sce | 42 +-- .../general_tests/lsqnonneg/lsqnonneg_maxiter.sce | 42 +++ tests/general_tests/lsqnonneg/lsqnonneg_param1.sce | 20 +- tests/general_tests/lsqnonneg/lsqnonneg_param2.sce | 20 +- tests/general_tests/lsqnonneg/lsqnonneg_zeros.sce | 28 ++ tests/general_tests/qpipopt/qpipopt_logical_2.sce | 1 - tests/general_tests/qpipopt/qpipopt_maxiter.sce | 46 ++++ tests/general_tests/qpipopt/qpipopt_zeros.sce | 42 +++ .../qpipoptmat/qpipoptmat_logical2.sce | 2 +- .../qpipoptmat/qpipoptmat_maxiter.sce | 44 +++ .../general_tests/qpipoptmat/qpipoptmat_zeros.sce | 35 +++ tests/general_tests/symphony/symphony_logical2.sce | 6 +- tests/general_tests/symphony/symphony_zeros.sce | 46 ++++ .../coin/.IpIpoptCalculatedQuantities.hpp.swp | Bin 0 -> 16384 bytes 104 files changed, 1497 insertions(+), 1273 deletions(-) create mode 100644 sci_gateway/cpp/.sci_QuadNLP.cpp.swp delete mode 100644 sci_gateway/cpp/libsymphonytools.c delete mode 100755 sci_gateway/cpp/libsymphonytools.so delete mode 100644 sci_gateway/cpp/template.cpp create mode 100644 tests/general_tests/lsqlin/lsqlin_maxiter.sce create mode 100644 tests/general_tests/lsqlin/lsqlin_zeros.sce create mode 100644 tests/general_tests/lsqnonneg/lsqnonneg_maxiter.sce create mode 100644 tests/general_tests/lsqnonneg/lsqnonneg_zeros.sce create mode 100644 tests/general_tests/qpipopt/qpipopt_maxiter.sce create mode 100644 tests/general_tests/qpipopt/qpipopt_zeros.sce create mode 100644 tests/general_tests/qpipoptmat/qpipoptmat_maxiter.sce create mode 100644 tests/general_tests/qpipoptmat/qpipoptmat_zeros.sce create mode 100644 tests/general_tests/symphony/symphony_zeros.sce create mode 100644 thirdparty/linux/include/coin/.IpIpoptCalculatedQuantities.hpp.swp diff --git a/demos/lsqlin.dem.sce b/demos/lsqlin.dem.sce index 0b81630..827ba63 100644 --- a/demos/lsqlin.dem.sce +++ b/demos/lsqlin.dem.sce @@ -4,46 +4,40 @@ mode(1) // //A simple linear least square example -C = [0.9501 0.7620 0.6153 0.4057 -0.2311 0.4564 0.7919 0.9354 -0.6068 0.0185 0.9218 0.9169 -0.4859 0.8214 0.7382 0.4102 -0.8912 0.4447 0.1762 0.8936]; -d = [0.0578 -0.3528 -0.8131 -0.0098 -0.1388]; -A = [0.2027 0.2721 0.7467 0.4659 -0.1987 0.1988 0.4450 0.4186 -0.6037 0.0152 0.9318 0.8462]; -b = [0.5251 -0.2026 -0.6721]; +C = [ 2 0; +-1 1; +0 2] +d = [1 +0 +-1]; +A = [10 -2; +-2 10]; +b = [4 +-4]; [xopt,resnorm,residual,exitflag,output,lambda] = lsqlin(C,d,A,b) // Press ENTER to continue halt() // Press return to continue //A basic example for equality, inequality constraints and variable bounds -C = [0.9501 0.7620 0.6153 0.4057 -0.2311 0.4564 0.7919 0.9354 -0.6068 0.0185 0.9218 0.9169 -0.4859 0.8214 0.7382 0.4102 -0.8912 0.4447 0.1762 0.8936]; -d = [0.0578 -0.3528 -0.8131 -0.0098 -0.1388]; -A =[0.2027 0.2721 0.7467 0.4659 -0.1987 0.1988 0.4450 0.4186 -0.6037 0.0152 0.9318 0.8462]; -b =[0.5251 -0.2026 -0.6721]; -Aeq = [3 5 7 9]; -beq = 4; -lb = -0.1*ones(4,1); -ub = 2*ones(4,1); +C = [1 1 1; +1 1 0; +0 1 1; +1 0 0; +0 0 1] +d = [89; +67; +53; +35; +20;] +A = [3 2 1; +2 3 4; +1 2 3]; +b = [191 +209 +162]; +Aeq = [1 2 1]; +beq = 10; +lb = repmat(0.1,3,1); +ub = repmat(4,3,1); [xopt,resnorm,residual,exitflag,output,lambda] = lsqlin(C,d,A,b,Aeq,beq,lb,ub) //========= E N D === O F === D E M O =========// diff --git a/demos/lsqnonneg.dem.sce b/demos/lsqnonneg.dem.sce index 73fa6df..d8be39b 100644 --- a/demos/lsqnonneg.dem.sce +++ b/demos/lsqnonneg.dem.sce @@ -4,15 +4,15 @@ mode(1) // // A basic lsqnonneg problem -C = [ -0.0372 0.2869 -0.6861 0.7071 -0.6233 0.6245 -0.6344 0.6170]; -d = [ -0.8587 -0.1781 -0.0747 -0.8405]; +C = [1 1 1; +1 1 0; +0 1 1; +1 0 0; +0 0 1] +d = [89; +67; +53; +35; +20;] [xopt,resnorm,residual,exitflag,output,lambda] = lsqnonneg(C,d) //========= E N D === O F === D E M O =========// diff --git a/demos/qpipopt.dem.sce b/demos/qpipopt.dem.sce index 8e405e1..3fa7476 100644 --- a/demos/qpipopt.dem.sce +++ b/demos/qpipopt.dem.sce @@ -3,6 +3,28 @@ mode(1) // Demo of qpipopt.sci // +//Ref : example 14 : +//https://www.me.utexas.edu/~jensen/ORMM/supplements/methods/nlpmethod/S2_quadratic.pdf +// min. -8*x1*x1 -16*x2*x2 + x1 + 4*x2 +// such that +// x1 + x2 <= 5, +// x1 <= 3, +// x1 >= 0, +// x2 >= 0 +H = [2 0 +0 8]; +f = [-8; -16]; +A = [1 1;1 0]; +conUB = [5;3]; +conLB = [-%inf; -%inf]; +lb = [0; 0]; +ub = [%inf; %inf]; +nbVar = 2; +nbCon = 2; +[xopt,fopt,exitflag,output,lambda] = qpipopt(nbVar,nbCon,H,f,lb,ub,A,conLB,conUB) +//Press ENTER to continue +halt() // Press return to continue + //Find x in R^6 such that: A= [1,-1,1,0,3,1; -1,0,-3,-4,5,6; @@ -20,24 +42,4 @@ nbCon = 5; x0 = repmat(0,nbVar,1); param = list("MaxIter", 300, "CpuTime", 100); [xopt,fopt,exitflag,output,lambda]=qpipopt(nbVar,nbCon,H,f,lb,ub,A,conLB,conUB,x0,param) -// Press ENTER to continue -halt() // Press return to continue - -//Find the value of x that minimize following function -// f(x) = 0.5*x1^2 + x2^2 - x1*x2 - 2*x1 - 6*x2 -// Subject to: -// x1 + x2 ≤ 2 -// –x1 + 2x2 ≤ 2 -// 2x1 + x2 ≤ 3 -// 0 ≤ x1, 0 ≤ x2. -H = [1 -1; -1 2]; -f = [-2; -6]; -A = [1 1; -1 2; 2 1]; -conUB = [2; 2; 3]; -conLB = [-%inf; -%inf; -%inf]; -lb = [0; 0]; -ub = [%inf; %inf]; -nbVar = 2; -nbCon = 3; -[xopt,fopt,exitflag,output,lambda] = qpipopt(nbVar,nbCon,H,f,lb,ub,A,conLB,conUB) //========= E N D === O F === D E M O =========// diff --git a/demos/qpipoptmat.dem.sce b/demos/qpipoptmat.dem.sce index 0892855..1e8ebc8 100644 --- a/demos/qpipoptmat.dem.sce +++ b/demos/qpipoptmat.dem.sce @@ -3,17 +3,19 @@ mode(1) // Demo of qpipoptmat.sci // -//Find the value of x that minimize following function -// f(x) = 0.5*x1^2 + x2^2 - x1*x2 - 2*x1 - 6*x2 -// Subject to: -// x1 + x2 ≤ 2 -// –x1 + 2x2 ≤ 2 -// 2x1 + x2 ≤ 3 -// 0 ≤ x1, 0 ≤ x2. -H = [1 -1; -1 2]; -f = [-2; -6]; -A = [1 1; -1 2; 2 1]; -b = [2; 2; 3]; +//Ref : example 14 : +//https://www.me.utexas.edu/~jensen/ORMM/supplements/methods/nlpmethod/S2_quadratic.pdf +// min. -8*x1*x1 -16*x2*x2 + x1 + 4*x2 +// such that +// x1 + x2 <= 5, +// x1 <= 3, +// x1 >= 0, +// x2 >= 0 +H = [2 0 +0 8]; +f = [-8; -16]; +A = [1 1;1 0]; +b = [5;3]; lb = [0; 0]; ub = [%inf; %inf]; [xopt,fopt,exitflag,output,lambda] = qpipoptmat(H,f,A,b,[],[],lb,ub) @@ -34,5 +36,5 @@ x0 = repmat(0,6,1); param = list("MaxIter", 300, "CpuTime", 100); //and minimize 0.5*x'*H*x + f'*x with f=[1; 2; 3; 4; 5; 6]; H=eye(6,6); -[xopt,fopt,exitflag,output,lambda]=qpipoptmat(H,f,A,b,Aeq,beq,lb,ub,[],param) +[xopt,fopt,exitflag,output,lambda]=qpipoptmat(H,f,A,b,Aeq,beq,lb,ub,x0,param) //========= E N D === O F === D E M O =========// diff --git a/demos/symphony.dem.sce b/demos/symphony.dem.sce index 67886a3..a0b629e 100644 --- a/demos/symphony.dem.sce +++ b/demos/symphony.dem.sce @@ -3,7 +3,7 @@ mode(1) // Demo of symphony.sci // -//A basic case : +//Reference: Westerberg, Carl-Henrik, Bengt Bjorklund, and Eskil Hultman. "An application of mixed integer programming in a Swedish steel mill." Interfaces 7, no. 2 (1977): 39-43. // Objective function c = [350*5,330*3,310*4,280*6,500,450,400,100]'; // Lower Bound of variable diff --git a/demos/symphonymat.dem.sce b/demos/symphonymat.dem.sce index a5e19ac..7bc751e 100644 --- a/demos/symphonymat.dem.sce +++ b/demos/symphonymat.dem.sce @@ -4,6 +4,7 @@ mode(1) // // Objective function +// Reference: Westerberg, Carl-Henrik, Bengt Bjorklund, and Eskil Hultman. "An application of mixed integer programming in a Swedish steel mill." Interfaces 7, no. 2 (1977): 39-43. c = [350*5,330*3,310*4,280*6,500,450,400,100]'; // Lower Bound of variable lb = repmat(0,1,8); diff --git a/help/en_US/lsqlin.xml b/help/en_US/lsqlin.xml index c08905e..c6ec286 100644 --- a/help/en_US/lsqlin.xml +++ b/help/en_US/lsqlin.xml @@ -62,13 +62,13 @@ resnorm : a double, objective value returned as the scalar value norm(C*x-d)^2. residual : - a vector of double, solution residuals returned as the vector C*x-d. + a vector of double, solution residuals returned as the vector d-C*x. exitflag : - Integer identifying the reason the algorithm terminated. It could be 0, 1 or 2 etc. i.e. Optimal, Maximum Number of Iterations Exceeded, CPU time exceeded. Other flags one can see in the lsqlin macro. + A flag showing returned exit flag from Ipopt. It could be 0, 1 or 2 etc. i.e. Optimal, Maximum Number of Iterations Exceeded, CPU time exceeded. Other flags one can see in the lsqlin macro. output : Structure containing information about the optimization. This version only contains number of iterations. lambda : - Structure containing the Lagrange multipliers at the solution x (separated by constraint type).It contains lower, upper bound multiplier and linear equality, inequality constraints. + Structure containing the Lagrange multipliers at the solution x (separated by constraint type).It contains lower, upper bound multiplier and linear equality, inequality constraint multiplier. @@ -99,22 +99,16 @@ The routine calls Ipopt for solving the linear least square problem, Ipopt is a Examples Examples diff --git a/help/en_US/lsqnonneg.xml b/help/en_US/lsqnonneg.xml index 662ba2a..5d78bbd 100644 --- a/help/en_US/lsqnonneg.xml +++ b/help/en_US/lsqnonneg.xml @@ -43,13 +43,13 @@ resnorm : a double, objective value returned as the scalar value norm(C*x-d)^2. residual : - a vector of double, solution residuals returned as the vector C*x-d. + a vector of double, solution residuals returned as the vector d-C*x. exitflag : - Integer identifying the reason the algorithm terminated. It could be 0, 1 or 2 i.e. Optimal, Maximum Number of Iterations Exceeded, CPU time exceeded. + A flag showing returned exit flag from Ipopt. It could be 0, 1 or 2 etc. i.e. Optimal, Maximum Number of Iterations Exceeded, CPU time exceeded. Other flags one can see in the lsqlin macro. output : Structure containing information about the optimization. This version only contains number of iterations. lambda : - Structure containing the Lagrange multipliers at the solution x. It contains lower and upper bound multiplier. + Structure containing the Lagrange multipliers at the solution xopt. It contains lower, upper bound multiplier and linear equality, inequality constraint multiplier. @@ -78,16 +78,16 @@ The routine calls Ipopt for solving the nonnegative least-squares curve fitting Examples diff --git a/help/en_US/qpipopt.xml b/help/en_US/qpipopt.xml index 6dd578d..3ba2107 100644 --- a/help/en_US/qpipopt.xml +++ b/help/en_US/qpipopt.xml @@ -62,11 +62,11 @@ fopt : a double, the function value at x. exitflag : - Integer identifying the reason the algorithm terminated. It could be 0, 1 or 2 etc. i.e. Optimal, Maximum Number of Iterations Exceeded, CPU time exceeded. Other flags one can see in the qpipopt macro. + A flag showing returned exit flag from Ipopt. It could be 0, 1 or 2 etc. i.e. Optimal, Maximum Number of Iterations Exceeded, CPU time exceeded. Other flags one can see in the lsqlin macro. output : Structure containing information about the optimization. This version only contains number of iterations lambda : - Structure containing the Lagrange multipliers at the solution x (separated by constraint type).It contains lower, upper and linear equality, inequality constraints. + Structure containing the Lagrange multipliers at the solution x (separated by constraint type).It contains lower, upper bound multiplier and linear equality, inequality constraint multiplier. @@ -74,7 +74,6 @@ Description Search the minimum of a constrained linear quadratic optimization problem specified by : -find the minimum of f(x) such that @@ -93,6 +92,33 @@ The routine calls Ipopt for solving the quadratic problem, Ipopt is a library wr + + Examples + = 0, +// x2 >= 0 +H = [2 0 +0 8]; +f = [-8; -16]; +A = [1 1;1 0]; +conUB = [5;3]; +conLB = [-%inf; -%inf]; +lb = [0; 0]; +ub = [%inf; %inf]; +nbVar = 2; +nbCon = 2; +[xopt,fopt,exitflag,output,lambda] = qpipopt(nbVar,nbCon,H,f,lb,ub,A,conLB,conUB) +//Press ENTER to continue + + ]]> + + Examples - - - - Examples - diff --git a/help/en_US/qpipoptmat.xml b/help/en_US/qpipoptmat.xml index 8d0bc0c..1334603 100644 --- a/help/en_US/qpipoptmat.xml +++ b/help/en_US/qpipoptmat.xml @@ -62,12 +62,14 @@ a vector of double, the computed solution of the optimization problem. fopt : a double, the function value at x. + residual : + a vector of double, solution residuals returned as the vector d-C*x. exitflag : - Integer identifying the reason the algorithm terminated.It could be 0, 1 or 2 etc. i.e. Optimal, Maximum Number of Iterations Exceeded, CPU time exceeded. Other flags one can see in the qpipoptmat macro. + A flag showing returned exit flag from Ipopt. It could be 0, 1 or 2 etc. i.e. Optimal, Maximum Number of Iterations Exceeded, CPU time exceeded. Other flags one can see in the lsqlin macro. output : Structure containing information about the optimization. This version only contains number of iterations. lambda : - Structure containing the Lagrange multipliers at the solution x (separated by constraint type).It contains lower, upper and linear equality, inequality constraints. + Structure containing the Lagrange multipliers at the solution x (separated by constraint type).It contains lower, upper bound multiplier and linear equality, inequality constraint multiplier. @@ -75,7 +77,6 @@ Description Search the minimum of a constrained linear quadratic optimization problem specified by : -find the minimum of f(x) such that @@ -98,17 +99,19 @@ The routine calls Ipopt for solving the quadratic problem, Ipopt is a library wr Examples = 0, +// x2 >= 0 +H = [2 0 +0 8]; +f = [-8; -16]; +A = [1 1;1 0]; +b = [5;3]; lb = [0; 0]; ub = [%inf; %inf]; [xopt,fopt,exitflag,output,lambda] = qpipoptmat(H,f,A,b,[],[],lb,ub) @@ -134,7 +137,7 @@ x0 = repmat(0,6,1); param = list("MaxIter", 300, "CpuTime", 100); //and minimize 0.5*x'*H*x + f'*x with f=[1; 2; 3; 4; 5; 6]; H=eye(6,6); -[xopt,fopt,exitflag,output,lambda]=qpipoptmat(H,f,A,b,Aeq,beq,lb,ub,[],param) +[xopt,fopt,exitflag,output,lambda]=qpipoptmat(H,f,A,b,Aeq,beq,lb,ub,x0,param) ]]> diff --git a/help/en_US/scilab_en_US_help/JavaHelpSearch/DOCS b/help/en_US/scilab_en_US_help/JavaHelpSearch/DOCS index 8ebf21b..d3146bf 100644 Binary files a/help/en_US/scilab_en_US_help/JavaHelpSearch/DOCS and b/help/en_US/scilab_en_US_help/JavaHelpSearch/DOCS differ diff --git a/help/en_US/scilab_en_US_help/JavaHelpSearch/DOCS.TAB b/help/en_US/scilab_en_US_help/JavaHelpSearch/DOCS.TAB index 728f68c..f0a1fcb 100644 Binary files a/help/en_US/scilab_en_US_help/JavaHelpSearch/DOCS.TAB and b/help/en_US/scilab_en_US_help/JavaHelpSearch/DOCS.TAB differ diff --git a/help/en_US/scilab_en_US_help/JavaHelpSearch/OFFSETS b/help/en_US/scilab_en_US_help/JavaHelpSearch/OFFSETS index 9468d7f..8a63187 100644 Binary files a/help/en_US/scilab_en_US_help/JavaHelpSearch/OFFSETS and b/help/en_US/scilab_en_US_help/JavaHelpSearch/OFFSETS differ diff --git a/help/en_US/scilab_en_US_help/JavaHelpSearch/POSITIONS b/help/en_US/scilab_en_US_help/JavaHelpSearch/POSITIONS index d5dee46..423d132 100644 Binary files a/help/en_US/scilab_en_US_help/JavaHelpSearch/POSITIONS and b/help/en_US/scilab_en_US_help/JavaHelpSearch/POSITIONS differ diff --git a/help/en_US/scilab_en_US_help/JavaHelpSearch/SCHEMA b/help/en_US/scilab_en_US_help/JavaHelpSearch/SCHEMA index e2b33d1..6df2edb 100644 --- a/help/en_US/scilab_en_US_help/JavaHelpSearch/SCHEMA +++ b/help/en_US/scilab_en_US_help/JavaHelpSearch/SCHEMA @@ -1,2 +1,2 @@ JavaSearch 1.0 -TMAP bs=2048 rt=1 fl=-1 id1=1446 id2=1 +TMAP bs=2048 rt=1 fl=-1 id1=1249 id2=1 diff --git a/help/en_US/scilab_en_US_help/JavaHelpSearch/TMAP b/help/en_US/scilab_en_US_help/JavaHelpSearch/TMAP index 35dc462..6104335 100644 Binary files a/help/en_US/scilab_en_US_help/JavaHelpSearch/TMAP and b/help/en_US/scilab_en_US_help/JavaHelpSearch/TMAP differ diff --git a/help/en_US/scilab_en_US_help/lsqlin.html b/help/en_US/scilab_en_US_help/lsqlin.html index aef773d..e9253c5 100644 --- a/help/en_US/scilab_en_US_help/lsqlin.html +++ b/help/en_US/scilab_en_US_help/lsqlin.html @@ -70,13 +70,13 @@
resnorm :

a double, objective value returned as the scalar value norm(C*x-d)^2.

residual : -

a vector of double, solution residuals returned as the vector C*x-d.

+

a vector of double, solution residuals returned as the vector d-C*x.

exitflag : -

Integer identifying the reason the algorithm terminated. It could be 0, 1 or 2 etc. i.e. Optimal, Maximum Number of Iterations Exceeded, CPU time exceeded. Other flags one can see in the lsqlin macro.

+

A flag showing returned exit flag from Ipopt. It could be 0, 1 or 2 etc. i.e. Optimal, Maximum Number of Iterations Exceeded, CPU time exceeded. Other flags one can see in the lsqlin macro.

output :

Structure containing information about the optimization. This version only contains number of iterations.

lambda : -

Structure containing the Lagrange multipliers at the solution x (separated by constraint type).It contains lower, upper bound multiplier and linear equality, inequality constraints.

+

Structure containing the Lagrange multipliers at the solution x (separated by constraint type).It contains lower, upper bound multiplier and linear equality, inequality constraint multiplier.

Description

Search the minimum of a constrained linear least square problem specified by :

@@ -86,47 +86,41 @@

Examples

//A simple linear least square example
-C = [0.9501    0.7620    0.6153    0.4057
-0.2311    0.4564    0.7919    0.9354
-0.6068    0.0185    0.9218    0.9169
-0.4859    0.8214    0.7382    0.4102
-0.8912    0.4447    0.1762    0.8936];
-d = [0.0578
-0.3528
-0.8131
-0.0098
-0.1388];
-A = [0.2027    0.2721    0.7467    0.4659
-0.1987    0.1988    0.4450    0.4186
-0.6037    0.0152    0.9318    0.8462];
-b = [0.5251
-0.2026
-0.6721];
+C = [ 2 0;
+-1 1;
+0 2]
+d = [1
+0
+-1];
+A = [10 -2;
+-2 10];
+b = [4
+-4];
 [xopt,resnorm,residual,exitflag,output,lambda] = lsqlin(C,d,A,b)
 // Press ENTER to continue

Examples

//A basic example for equality, inequality constraints and variable bounds
-C = [0.9501    0.7620    0.6153    0.4057
-0.2311    0.4564    0.7919    0.9354
-0.6068    0.0185    0.9218    0.9169
-0.4859    0.8214    0.7382    0.4102
-0.8912    0.4447    0.1762    0.8936];
-d = [0.0578
-0.3528
-0.8131
-0.0098
-0.1388];
-A =[0.2027    0.2721    0.7467    0.4659
-0.1987    0.1988    0.4450    0.4186
-0.6037    0.0152    0.9318    0.8462];
-b =[0.5251
-0.2026
-0.6721];
-Aeq = [3 5 7 9];
-beq = 4;
-lb = -0.1*ones(4,1);
-ub = 2*ones(4,1);
+C = [1 1 1;
+1 1 0;
+0 1 1;
+1 0 0;
+0 0 1]
+d = [89;
+67;
+53;
+35;
+20;]
+A = [3 2 1;
+2 3 4;
+1 2 3];
+b = [191
+209
+162];
+Aeq = [1 2 1];
+beq = 10;
+lb = repmat(0.1,3,1);
+ub = repmat(4,3,1);
 [xopt,resnorm,residual,exitflag,output,lambda] = lsqlin(C,d,A,b,Aeq,beq,lb,ub)

Authors

diff --git a/help/en_US/scilab_en_US_help/lsqnonneg.html b/help/en_US/scilab_en_US_help/lsqnonneg.html index e9a9110..7211c40 100644 --- a/help/en_US/scilab_en_US_help/lsqnonneg.html +++ b/help/en_US/scilab_en_US_help/lsqnonneg.html @@ -51,13 +51,13 @@
resnorm :

a double, objective value returned as the scalar value norm(C*x-d)^2.

residual : -

a vector of double, solution residuals returned as the vector C*x-d.

+

a vector of double, solution residuals returned as the vector d-C*x.

exitflag : -

Integer identifying the reason the algorithm terminated. It could be 0, 1 or 2 i.e. Optimal, Maximum Number of Iterations Exceeded, CPU time exceeded.

+

A flag showing returned exit flag from Ipopt. It could be 0, 1 or 2 etc. i.e. Optimal, Maximum Number of Iterations Exceeded, CPU time exceeded. Other flags one can see in the lsqlin macro.

output :

Structure containing information about the optimization. This version only contains number of iterations.

lambda : -

Structure containing the Lagrange multipliers at the solution x. It contains lower and upper bound multiplier.

+

Structure containing the Lagrange multipliers at the solution xopt. It contains lower, upper bound multiplier and linear equality, inequality constraint multiplier.

Description

Solves nonnegative least-squares curve fitting problems specified by :

@@ -67,16 +67,16 @@

Examples

// A basic lsqnonneg problem
-C = [
-0.0372    0.2869
-0.6861    0.7071
-0.6233    0.6245
-0.6344    0.6170];
-d = [
-0.8587
-0.1781
-0.0747
-0.8405];
+C = [1 1 1;
+1 1 0;
+0 1 1;
+1 0 0;
+0 0 1]
+d = [89;
+67;
+53;
+35;
+20;]
 [xopt,resnorm,residual,exitflag,output,lambda] = lsqnonneg(C,d)

Authors

diff --git a/help/en_US/scilab_en_US_help/qpipopt.html b/help/en_US/scilab_en_US_help/qpipopt.html index 349bbc4..31f389f 100644 --- a/help/en_US/scilab_en_US_help/qpipopt.html +++ b/help/en_US/scilab_en_US_help/qpipopt.html @@ -70,19 +70,40 @@
fopt :

a double, the function value at x.

exitflag : -

Integer identifying the reason the algorithm terminated. It could be 0, 1 or 2 etc. i.e. Optimal, Maximum Number of Iterations Exceeded, CPU time exceeded. Other flags one can see in the qpipopt macro.

+

A flag showing returned exit flag from Ipopt. It could be 0, 1 or 2 etc. i.e. Optimal, Maximum Number of Iterations Exceeded, CPU time exceeded. Other flags one can see in the lsqlin macro.

output :

Structure containing information about the optimization. This version only contains number of iterations

lambda : -

Structure containing the Lagrange multipliers at the solution x (separated by constraint type).It contains lower, upper and linear equality, inequality constraints.

+

Structure containing the Lagrange multipliers at the solution x (separated by constraint type).It contains lower, upper bound multiplier and linear equality, inequality constraint multiplier.

Description

-

Search the minimum of a constrained linear quadratic optimization problem specified by : -find the minimum of f(x) such that

+

Search the minimum of a constrained linear quadratic optimization problem specified by :

The routine calls Ipopt for solving the quadratic problem, Ipopt is a library written in C++.

+

Examples

+ 
//Ref : example 14 :
+//https://www.me.utexas.edu/~jensen/ORMM/supplements/methods/nlpmethod/S2_quadratic.pdf
+// min. -8*x1*x1 -16*x2*x2 + x1 + 4*x2
+// such that
+//    x1 + x2 <= 5,
+//    x1 <= 3,
+//    x1 >= 0,
+//    x2 >= 0
+H = [2 0
+0 8];
+f = [-8; -16];
+A = [1 1;1 0];
+conUB = [5;3];
+conLB = [-%inf; -%inf];
+lb = [0; 0];
+ub = [%inf; %inf];
+nbVar = 2;
+nbCon = 2;
+[xopt,fopt,exitflag,output,lambda] = qpipopt(nbVar,nbCon,H,f,lb,ub,A,conLB,conUB)
+//Press ENTER to continue
+

Examples

//Find x in R^6 such that:
 A= [1,-1,1,0,3,1;
@@ -100,27 +121,7 @@ find the minimum of f(x) such that

 nbCon = 5;
 x0 = repmat(0,nbVar,1);
 param = list("MaxIter", 300, "CpuTime", 100);
-[xopt,fopt,exitflag,output,lambda]=qpipopt(nbVar,nbCon,H,f,lb,ub,A,conLB,conUB,x0,param)
-// Press ENTER to continue
- -

Examples

- 
//Find the value of x that minimize following function
-// f(x) = 0.5*x1^2 + x2^2 - x1*x2 - 2*x1 - 6*x2
-// Subject to:
-// x1 + x2 ≤ 2
-// –x1 + 2x2 ≤ 2
-// 2x1 + x2 ≤ 3
-// 0 ≤ x1, 0 ≤ x2.
-H = [1 -1; -1 2];
-f = [-2; -6];
-A = [1 1; -1 2; 2 1];
-conUB = [2; 2; 3];
-conLB = [-%inf; -%inf; -%inf];
-lb = [0; 0];
-ub = [%inf; %inf];
-nbVar = 2;
-nbCon = 3;
-[xopt,fopt,exitflag,output,lambda] = qpipopt(nbVar,nbCon,H,f,lb,ub,A,conLB,conUB)
+[xopt,fopt,exitflag,output,lambda]=qpipopt(nbVar,nbCon,H,f,lb,ub,A,conLB,conUB,x0,param)

Authors

  • Keyur Joshi, Saikiran, Iswarya, Harpreet Singh
diff --git a/help/en_US/scilab_en_US_help/qpipoptmat.html b/help/en_US/scilab_en_US_help/qpipoptmat.html index 73c7298..6c195ea 100644 --- a/help/en_US/scilab_en_US_help/qpipoptmat.html +++ b/help/en_US/scilab_en_US_help/qpipoptmat.html @@ -70,32 +70,35 @@

a vector of double, the computed solution of the optimization problem.

fopt :

a double, the function value at x.

+
residual : +

a vector of double, solution residuals returned as the vector d-C*x.

exitflag : -

Integer identifying the reason the algorithm terminated.It could be 0, 1 or 2 etc. i.e. Optimal, Maximum Number of Iterations Exceeded, CPU time exceeded. Other flags one can see in the qpipoptmat macro.

+

A flag showing returned exit flag from Ipopt. It could be 0, 1 or 2 etc. i.e. Optimal, Maximum Number of Iterations Exceeded, CPU time exceeded. Other flags one can see in the lsqlin macro.

output :

Structure containing information about the optimization. This version only contains number of iterations.

lambda : -

Structure containing the Lagrange multipliers at the solution x (separated by constraint type).It contains lower, upper and linear equality, inequality constraints.

+

Structure containing the Lagrange multipliers at the solution x (separated by constraint type).It contains lower, upper bound multiplier and linear equality, inequality constraint multiplier.

Description

-

Search the minimum of a constrained linear quadratic optimization problem specified by : -find the minimum of f(x) such that

+

Search the minimum of a constrained linear quadratic optimization problem specified by :

The routine calls Ipopt for solving the quadratic problem, Ipopt is a library written in C++.

Examples

- 
//Find the value of x that minimize following function
-// f(x) = 0.5*x1^2 + x2^2 - x1*x2 - 2*x1 - 6*x2
-// Subject to:
-// x1 + x2 ≤ 2
-// –x1 + 2x2 ≤ 2
-// 2x1 + x2 ≤ 3
-// 0 ≤ x1, 0 ≤ x2.
-H = [1 -1; -1 2];
-f = [-2; -6];
-A = [1 1; -1 2; 2 1];
-b = [2; 2; 3];
+   
//Ref : example 14 :
+//https://www.me.utexas.edu/~jensen/ORMM/supplements/methods/nlpmethod/S2_quadratic.pdf
+// min. -8*x1*x1 -16*x2*x2 + x1 + 4*x2
+// such that
+//    x1 + x2 <= 5,
+//    x1 <= 3,
+//    x1 >= 0,
+//    x2 >= 0
+H = [2 0
+0 8];
+f = [-8; -16];
+A = [1 1;1 0];
+b = [5;3];
 lb = [0; 0];
 ub = [%inf; %inf];
 [xopt,fopt,exitflag,output,lambda] = qpipoptmat(H,f,A,b,[],[],lb,ub)
@@ -116,7 +119,7 @@ find the minimum of f(x) such that

 param = list("MaxIter", 300, "CpuTime", 100);
 //and minimize 0.5*x'*H*x + f'*x with
 f=[1; 2; 3; 4; 5; 6]; H=eye(6,6);
-[xopt,fopt,exitflag,output,lambda]=qpipoptmat(H,f,A,b,Aeq,beq,lb,ub,[],param)

+[xopt,fopt,exitflag,output,lambda]=qpipoptmat(H,f,A,b,Aeq,beq,lb,ub,x0,param)

Authors

  • Keyur Joshi, Saikiran, Iswarya, Harpreet Singh
diff --git a/help/en_US/scilab_en_US_help/symphony.html b/help/en_US/scilab_en_US_help/symphony.html index b81bbb0..e374a30 100644 --- a/help/en_US/scilab_en_US_help/symphony.html +++ b/help/en_US/scilab_en_US_help/symphony.html @@ -70,19 +70,18 @@
fopt :

a double, the function value at x.

status : -

status flag from symphony. 227 is optimal, 228 is Time limit exceeded, 230 is iteration limit exceeded.

+

status flag returned from symphony. 227 is optimal, 228 is Time limit exceeded, 230 is iteration limit exceeded.

output :

The output data structure contains detailed information about the optimization process. This version only contains number of iterations

Description

-

Search the minimum or maximum of a constrained mixed integer linear programming optimization problem specified by : -find the minimum or maximum of f(x) such that

+

Search the minimum or maximum of a constrained mixed integer linear programming optimization problem specified by :

The routine calls SYMPHONY written in C by gateway files for the actual computation.

Examples

- 
//A basic case :
+   
//Reference: Westerberg, Carl-Henrik, Bengt Bjorklund, and Eskil Hultman. "An application of mixed integer programming in a Swedish steel mill." Interfaces 7, no. 2 (1977): 39-43.
 // Objective function
 c = [350*5,330*3,310*4,280*6,500,450,400,100]';
 // Lower Bound of variable
diff --git a/help/en_US/scilab_en_US_help/symphonymat.html b/help/en_US/scilab_en_US_help/symphonymat.html
index fc60daf..203f2d4 100644
--- a/help/en_US/scilab_en_US_help/symphonymat.html
+++ b/help/en_US/scilab_en_US_help/symphonymat.html
@@ -67,19 +67,19 @@
    
fopt :
       
a double, the function value at x

    
status :
-      
status flag from symphony. 227 is optimal, 228 is Time limit exceeded, 230 is iteration limit exceeded.

+      
status flag returned from symphony. 227 is optimal, 228 is Time limit exceeded, 230 is iteration limit exceeded.

    
output :
       
The output data structure contains detailed information about the optimization process. This version only contains number of iterations.

 
 
Description

-   
Search the minimum or maximum of a constrained mixed integer linear programming optimization problem specified by :
-find the minimum or maximum of C'⋅x such that

+   
Search the minimum or maximum of a constrained mixed integer linear programming optimization problem specified by :

    

    
The routine calls SYMPHONY written in C by gateway files for the actual computation.

    

 
 
Examples

    
// Objective function
+// Reference: Westerberg, Carl-Henrik, Bengt Bjorklund, and Eskil Hultman. "An application of mixed integer programming in a Swedish steel mill." Interfaces 7, no. 2 (1977): 39-43.
 c = [350*5,330*3,310*4,280*6,500,450,400,100]';
 // Lower Bound of variable
 lb = repmat(0,1,8);
diff --git a/help/en_US/symphony.xml b/help/en_US/symphony.xml
index c0caa8e..da156ce 100644
--- a/help/en_US/symphony.xml
+++ b/help/en_US/symphony.xml
@@ -62,7 +62,7 @@
    fopt :
        a double, the function value at x.
    status :
-       status flag from symphony. 227 is optimal, 228 is Time limit exceeded, 230 is iteration limit exceeded.
+       status flag returned from symphony. 227 is optimal, 228 is Time limit exceeded, 230 is iteration limit exceeded.
    output :
        The output data structure contains detailed information about the optimization process. This version only contains number of iterations
    
@@ -72,7 +72,6 @@
    Description
    
 Search the minimum or maximum of a constrained mixed integer linear programming optimization problem specified by :
-find the minimum or maximum of f(x) such that
    
    
 
@@ -95,7 +94,7 @@ The routine calls SYMPHONY written in C by gateway files for the actual computat
 
    Examples
    fopt :
        a double, the function value at x
    status :
-       status flag from symphony. 227 is optimal, 228 is Time limit exceeded, 230 is iteration limit exceeded.
+       status flag returned from symphony. 227 is optimal, 228 is Time limit exceeded, 230 is iteration limit exceeded.
    output :
        The output data structure contains detailed information about the optimization process. This version only contains number of iterations.
    
@@ -69,7 +69,6 @@
    Description
    
 Search the minimum or maximum of a constrained mixed integer linear programming optimization problem specified by :
-find the minimum or maximum of C'⋅x such that
    
    
 
@@ -94,6 +93,7 @@ The routine calls SYMPHONY written in C by gateway files for the actual computat
    Examples
    
-  //    \begin{eqnarray}
-  //    &\mbox{min}_{x}
-  //    & 1/2⋅x^T⋅H⋅x + f^T⋅x  \\
-  //    & \text{subject to} & conLB \leq A⋅x \leq conUB \\
-  //    & & lb \leq x \leq ub \\
-  //    \end{eqnarray}
-  //   
-  //   
-  //   The routine calls Ipopt for solving the quadratic problem, Ipopt is a library written in C++.
-  //
-  // Examples
-  //      //Find x in R^6 such that:
-  //      A= [1,-1,1,0,3,1;
-  //         -1,0,-3,-4,5,6;
-  //          2,5,3,0,1,0
-  //          0,1,0,1,2,-1;
-  //         -1,0,2,1,1,0];
-  //      conLB=[1;2;3;-%inf;-%inf];
-  //      conUB = [1;2;3;-1;2.5];
-  //      lb=[-1000;-10000; 0; -1000; -1000; -1000];
-  //      ub=[10000; 100; 1.5; 100; 100; 1000];
-  //      //and minimize 0.5*x'⋅H⋅x + f'⋅x with
-  //      f=[1; 2; 3; 4; 5; 6]; H=eye(6,6);
-  //      nbVar = 6;
-  //      nbCon = 5;
-  //      x0 = repmat(0,nbVar,1);
-  //	  param = list("MaxIter", 300, "CpuTime", 100);
-  //      [xopt,fopt,exitflag,output,lambda]=qpipopt(nbVar,nbCon,H,f,lb,ub,A,conLB,conUB,x0,param)
-  // // Press ENTER to continue
-  //    
-  // Examples 
-  //    //Find the value of x that minimize following function
-  //    // f(x) = 0.5*x1^2 + x2^2 - x1*x2 - 2*x1 - 6*x2
-  //    // Subject to:
-  //    // x1 + x2 ≤ 2
-  //    // –x1 + 2x2 ≤ 2
-  //    // 2x1 + x2 ≤ 3
-  //    // 0 ≤ x1, 0 ≤ x2.
-  //	H = [1 -1; -1 2]; 
-  //	f = [-2; -6];
-  //    A = [1 1; -1 2; 2 1];
-  //	conUB = [2; 2; 3];
-  //	conLB = [-%inf; -%inf; -%inf];
-  //	lb = [0; 0];
-  //	ub = [%inf; %inf];
-  //	nbVar = 2;
-  //	nbCon = 3;
-  //	[xopt,fopt,exitflag,output,lambda] = qpipopt(nbVar,nbCon,H,f,lb,ub,A,conLB,conUB)
-  // Authors
-  // Keyur Joshi, Saikiran, Iswarya, Harpreet Singh
+	// Solves a linear quadratic problem.
+	//
+	//   Calling Sequence
+	//   xopt = qpipopt(nbVar,nbCon,H,f,lb,ub,A,conLB,conUB)
+	//   xopt = qpipopt(nbVar,nbCon,H,f,lb,ub,A,conLB,conUB,x0)
+	//   xopt = qpipopt(nbVar,nbCon,H,f,lb,ub,A,conLB,conUB,x0,param)
+	//   [xopt,fopt,exitflag,output,lamda] = qpipopt( ... )
+	//   
+	//   Parameters
+	//   nbVar : a double, number of variables
+	//   nbCon : a double, number of constraints
+	//   H : a symmetric matrix of double, represents coefficients of quadratic in the quadratic problem.
+	//   f : a vector of double, represents coefficients of linear in the quadratic problem
+	//   lb : a vector of double, contains lower bounds of the variables.
+	//   ub : a vector of double, contains upper bounds of the variables.
+	//   A : a matrix of double, contains  matrix representing the constraint matrix 
+	//   conLB : a vector of double, contains lower bounds of the constraints. 
+	//   conUB : a vector of double, contains upper bounds of the constraints. 
+	//   x0 : a vector of double, contains initial guess of variables.
+	//   param : a list containing the the parameters to be set.
+	//   xopt : a vector of double, the computed solution of the optimization problem.
+	//   fopt : a double, the function value at x.
+	//   exitflag : A flag showing returned exit flag from Ipopt. It could be 0, 1 or 2 etc. i.e. Optimal, Maximum Number of Iterations Exceeded, CPU time exceeded. Other flags one can see in the lsqlin macro. 
+	//   output : Structure containing information about the optimization. This version only contains number of iterations
+	//   lambda : Structure containing the Lagrange multipliers at the solution x (separated by constraint type).It contains lower, upper bound multiplier and linear equality, inequality constraint multiplier.
+	//   
+	//   Description
+	//   Search the minimum of a constrained linear quadratic optimization problem specified by :
+	//
+	//   
+	//    \begin{eqnarray}
+	//    &\mbox{min}_{x}
+	//    & 1/2⋅x^T⋅H⋅x + f^T⋅x  \\
+	//    & \text{subject to} & conLB \leq A⋅x \leq conUB \\
+	//    & & lb \leq x \leq ub \\
+	//    \end{eqnarray}
+	//   
+	//   
+	//   The routine calls Ipopt for solving the quadratic problem, Ipopt is a library written in C++.
+	//
+	// Examples
+	//		//Ref : example 14 :
+	//		//https://www.me.utexas.edu/~jensen/ORMM/supplements/methods/nlpmethod/S2_quadratic.pdf
+	//		// min. -8*x1*x1 -16*x2*x2 + x1 + 4*x2
+	//		// such that
+	//		//	x1 + x2 <= 5,
+	//		//	x1 <= 3,
+	//		//	x1 >= 0,
+	//		//	x2 >= 0
+	//	H = [2 0
+	//		 0 8]; 
+	//	f = [-8; -16];
+	//  A = [1 1;1 0];
+	//	conUB = [5;3];
+	//	conLB = [-%inf; -%inf];
+	//	lb = [0; 0];
+	//	ub = [%inf; %inf];
+	//	nbVar = 2;
+	//	nbCon = 2;
+	//	[xopt,fopt,exitflag,output,lambda] = qpipopt(nbVar,nbCon,H,f,lb,ub,A,conLB,conUB)
+	//  //Press ENTER to continue
+	//    
+	// Examples 
+	//      //Find x in R^6 such that:
+	//      A= [1,-1,1,0,3,1;
+	//         -1,0,-3,-4,5,6;
+	//          2,5,3,0,1,0
+	//          0,1,0,1,2,-1;
+	//         -1,0,2,1,1,0];
+	//      conLB=[1;2;3;-%inf;-%inf];
+	//      conUB = [1;2;3;-1;2.5];
+	//      lb=[-1000;-10000; 0; -1000; -1000; -1000];
+	//      ub=[10000; 100; 1.5; 100; 100; 1000];
+	//      //and minimize 0.5*x'⋅H⋅x + f'⋅x with
+	//      f=[1; 2; 3; 4; 5; 6]; H=eye(6,6);
+	//      nbVar = 6;
+	//      nbCon = 5;
+	//      x0 = repmat(0,nbVar,1);
+	//	  param = list("MaxIter", 300, "CpuTime", 100);
+	//      [xopt,fopt,exitflag,output,lambda]=qpipopt(nbVar,nbCon,H,f,lb,ub,A,conLB,conUB,x0,param)
+	// Authors
+	// Keyur Joshi, Saikiran, Iswarya, Harpreet Singh
     
     
-//To check the number of input and output argument
-   [lhs , rhs] = argn();
+	//To check the number of input and output argument
+    [lhs , rhs] = argn();
 	
-//To check the number of argument given by user
-   if ( rhs < 9 | rhs > 11 ) then
-    errmsg = msprintf(gettext("%s: Unexpected number of input arguments : %d provided while should be 9, 10 or 11"), "qpipopt", rhs);
-    error(errmsg)
-   end
-   
+	//To check the number of argument given by user
+	if ( rhs < 9 | rhs > 11 ) then
+		errmsg = msprintf(gettext("%s: Unexpected number of input arguments : %d provided while should be 9, 10 or 11"), "qpipopt", rhs);
+		error(errmsg)
+	end
+
 	nbVar = [];
 	nbCon = [];
 	H = [];
diff --git a/macros/qpipoptmat.bin b/macros/qpipoptmat.bin
index 35142ae..817f0f9 100644
Binary files a/macros/qpipoptmat.bin and b/macros/qpipoptmat.bin differ
diff --git a/macros/qpipoptmat.sci b/macros/qpipoptmat.sci
index 8e9c67e..d019aa1 100644
--- a/macros/qpipoptmat.sci
+++ b/macros/qpipoptmat.sci
@@ -1,13 +1,13 @@
 // Copyright (C) 2015 - IIT Bombay - FOSSEE
 //
-// Author: Harpreet Singh
-// Organization: FOSSEE, IIT Bombay
-// Email: harpreet.mertia@gmail.com
 // This file must be used under the terms of the CeCILL.
 // This source file is licensed as described in the file COPYING, which
 // you should have received as part of this distribution.  The terms
 // are also available at
 // http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt
+// Author: Harpreet Singh
+// Organization: FOSSEE, IIT Bombay
+// Email: toolbox@scilab.in
 
 
 function [xopt,fopt,exitflag,output,lambda] = qpipoptmat (varargin)
@@ -35,13 +35,13 @@ function [xopt,fopt,exitflag,output,lambda] = qpipoptmat (varargin)
 	//   param : a list containing the the parameters to be set.
 	//   xopt : a vector of double, the computed solution of the optimization problem.
 	//   fopt : a double, the function value at x.
-	//   exitflag : Integer identifying the reason the algorithm terminated.It could be 0, 1 or 2 etc. i.e. Optimal, Maximum Number of Iterations Exceeded, CPU time exceeded. Other flags one can see in the qpipoptmat macro.
+	//   residual : a vector of double, solution residuals returned as the vector d-C*x.
+	//   exitflag : A flag showing returned exit flag from Ipopt. It could be 0, 1 or 2 etc. i.e. Optimal, Maximum Number of Iterations Exceeded, CPU time exceeded. Other flags one can see in the lsqlin macro. 
 	//   output : Structure containing information about the optimization. This version only contains number of iterations.
-	//   lambda : Structure containing the Lagrange multipliers at the solution x (separated by constraint type).It contains lower, upper and linear equality, inequality constraints.
+	//   lambda : Structure containing the Lagrange multipliers at the solution x (separated by constraint type).It contains lower, upper bound multiplier and linear equality, inequality constraint multiplier.
 	//   
 	//   Description
 	//   Search the minimum of a constrained linear quadratic optimization problem specified by :
-	//   find the minimum of f(x) such that 
 	//
 	//   
 	//    \begin{eqnarray}
@@ -56,17 +56,19 @@ function [xopt,fopt,exitflag,output,lambda] = qpipoptmat (varargin)
 	//   The routine calls Ipopt for solving the quadratic problem, Ipopt is a library written in C++.
 	//
 	// Examples
-	//    //Find the value of x that minimize following function
-	//    // f(x) = 0.5*x1^2 + x2^2 - x1*x2 - 2*x1 - 6*x2
-	//    // Subject to:
-	//    // x1 + x2 ≤ 2
-	//    // –x1 + 2x2 ≤ 2
-	//    // 2x1 + x2 ≤ 3
-	//    // 0 ≤ x1, 0 ≤ x2.
-	//	H = [1 -1; -1 2]; 
-	//	f = [-2; -6];
-	//  A = [1 1; -1 2; 2 1];
-	//	b = [2; 2; 3];
+	//		//Ref : example 14 :
+	//		//https://www.me.utexas.edu/~jensen/ORMM/supplements/methods/nlpmethod/S2_quadratic.pdf
+	//		// min. -8*x1*x1 -16*x2*x2 + x1 + 4*x2
+	//		// such that
+	//		//	x1 + x2 <= 5,
+	//		//	x1 <= 3,
+	//		//	x1 >= 0,
+	//		//	x2 >= 0
+	//	H = [2 0
+	//		 0 8]; 
+	//	f = [-8; -16];
+	//  A = [1 1;1 0];
+	//	b = [5;3];
 	//	lb = [0; 0];
 	//	ub = [%inf; %inf];
 	//	[xopt,fopt,exitflag,output,lambda] = qpipoptmat(H,f,A,b,[],[],lb,ub)
@@ -87,7 +89,7 @@ function [xopt,fopt,exitflag,output,lambda] = qpipoptmat (varargin)
 	//	  param = list("MaxIter", 300, "CpuTime", 100);
 	//    //and minimize 0.5*x'*H*x + f'*x with
 	//    f=[1; 2; 3; 4; 5; 6]; H=eye(6,6);
-	//    [xopt,fopt,exitflag,output,lambda]=qpipoptmat(H,f,A,b,Aeq,beq,lb,ub,[],param)
+	//    [xopt,fopt,exitflag,output,lambda]=qpipoptmat(H,f,A,b,Aeq,beq,lb,ub,x0,param)
 	// Authors
 	// Keyur Joshi, Saikiran, Iswarya, Harpreet Singh
     
diff --git a/macros/setOptions.sci b/macros/setOptions.sci
index 68aad02..995b2fb 100644
--- a/macros/setOptions.sci
+++ b/macros/setOptions.sci
@@ -1,13 +1,13 @@
 // Copyright (C) 2015 - IIT Bombay - FOSSEE
 //
-// Author: Harpreet Singh
-// Organization: FOSSEE, IIT Bombay
-// Email: harpreet.mertia@gmail.com
 // This file must be used under the terms of the CeCILL.
 // This source file is licensed as described in the file COPYING, which
 // you should have received as part of this distribution.  The terms
 // are also available at
 // http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt
+// Author: Harpreet Singh
+// Organization: FOSSEE, IIT Bombay
+// Email: toolbox@scilab.in
 
 function setOptions(varargin)
 
diff --git a/macros/symphony.bin b/macros/symphony.bin
index 9217660..87b6444 100644
Binary files a/macros/symphony.bin and b/macros/symphony.bin differ
diff --git a/macros/symphony.sci b/macros/symphony.sci
index 264a513..d465b90 100644
--- a/macros/symphony.sci
+++ b/macros/symphony.sci
@@ -1,13 +1,13 @@
 // Copyright (C) 2015 - IIT Bombay - FOSSEE
 //
-// Author: Harpreet Singh
-// Organization: FOSSEE, IIT Bombay
-// Email: harpreet.mertia@gmail.com
 // This file must be used under the terms of the CeCILL.
 // This source file is licensed as described in the file COPYING, which
 // you should have received as part of this distribution.  The terms
 // are also available at
 // http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt
+// Author: Harpreet Singh
+// Organization: FOSSEE, IIT Bombay
+// Email: toolbox@scilab.in
 
 function [xopt,fopt,status,output] = symphony (varargin)
 	// Solves a  mixed integer linear programming constrained optimization problem.
@@ -32,12 +32,11 @@ function [xopt,fopt,status,output] = symphony (varargin)
 	//   options : a list containing the the parameters to be set.
 	//   xopt : a vector of double, the computed solution of the optimization problem.
 	//   fopt : a double, the function value at x.
-	//   status : status flag from symphony. 227 is optimal, 228 is Time limit exceeded, 230 is iteration limit exceeded.
+	//   status : status flag returned from symphony. 227 is optimal, 228 is Time limit exceeded, 230 is iteration limit exceeded.
 	//   output : The output data structure contains detailed information about the optimization process. This version only contains number of iterations
 	//   
 	//   Description
 	//   Search the minimum or maximum of a constrained mixed integer linear programming optimization problem specified by :
-	//   find the minimum or maximum of f(x) such that 
 	//
 	//   
 	//    \begin{eqnarray}
@@ -52,7 +51,7 @@ function [xopt,fopt,status,output] = symphony (varargin)
 	//   The routine calls SYMPHONY written in C by gateway files for the actual computation.
 	//
 	//   Examples
-	//    //A basic case : 
+	//   //Reference: Westerberg, Carl-Henrik, Bengt Bjorklund, and Eskil Hultman. "An application of mixed integer programming in a Swedish steel mill." Interfaces 7, no. 2 (1977): 39-43.
 	//    // Objective function
 	//    c = [350*5,330*3,310*4,280*6,500,450,400,100]';
 	//    // Lower Bound of variable
@@ -203,8 +202,21 @@ function [xopt,fopt,status,output] = symphony (varargin)
       options = varargin(11);
    end
 
-// Check if the user gives row vector 
-// and Changing it to a column matrix
+	// Check if the user gives empty matrix
+    if (size(lb,2)==0) then
+        lb = repmat(-%inf,nbVar,1);
+    end
+    
+    if (size(isInt,2)==0) then
+        isInt = repmat(%f,nbVar,1);
+    end
+    
+    if (size(ub,2)==0) then
+        ub = repmat(%inf,nbVar,1);
+    end
+
+	// Check if the user gives row vector 
+	// and Changing it to a column matrix
 
    if (size(isInt,2)== [nbVar]) then
 	isInt = isInt';
@@ -262,7 +274,7 @@ function [xopt,fopt,status,output] = symphony (varargin)
    end
 
    //Check the column of constraint which should equal to the number of variables
-   if ( size(A,2) ~= nbVar) then
+   if ( size(A,2) ~= nbVar & size(A,2) ~= 0) then
       errmsg = msprintf(gettext("%s: The number of columns in constraint should equal to the number of variables"), "Symphony");
       error(errmsg);
    end
diff --git a/macros/symphony_call.sci b/macros/symphony_call.sci
index cfe73ae..af066f4 100644
--- a/macros/symphony_call.sci
+++ b/macros/symphony_call.sci
@@ -1,13 +1,13 @@
 // Copyright (C) 2015 - IIT Bombay - FOSSEE
 //
-// Author: Harpreet Singh
-// Organization: FOSSEE, IIT Bombay
-// Email: harpreet.mertia@gmail.com
 // This file must be used under the terms of the CeCILL.
 // This source file is licensed as described in the file COPYING, which
 // you should have received as part of this distribution.  The terms
 // are also available at
 // http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt
+// Author: Harpreet Singh
+// Organization: FOSSEE, IIT Bombay
+// Email: toolbox@scilab.in
 
 function [xopt,fopt,status,output] = symphony_call(nbVar,nbCon,objCoef,isInt,LB,UB,conMatrix,conLB,conUB,objSense,options)
 
diff --git a/macros/symphonymat.bin b/macros/symphonymat.bin
index 0841d41..eacbd5c 100644
Binary files a/macros/symphonymat.bin and b/macros/symphonymat.bin differ
diff --git a/macros/symphonymat.sci b/macros/symphonymat.sci
index 2c0c18d..67e64c5 100644
--- a/macros/symphonymat.sci
+++ b/macros/symphonymat.sci
@@ -1,162 +1,162 @@
 // Copyright (C) 2015 - IIT Bombay - FOSSEE
 //
-// Author: Harpreet Singh
-// Organization: FOSSEE, IIT Bombay
-// Email: harpreet.mertia@gmail.com
 // This file must be used under the terms of the CeCILL.
 // This source file is licensed as described in the file COPYING, which
 // you should have received as part of this distribution.  The terms
 // are also available at
 // http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt
+// Author: Harpreet Singh
+// Organization: FOSSEE, IIT Bombay
+// Email: toolbox@scilab.in
 
 function [xopt,fopt,status,iter] = symphonymat (varargin)
-  // Solves a mixed integer linear programming constrained optimization problem in intlinprog format.
-  //
-  //   Calling Sequence
-  //   xopt = symphonymat(c,intcon,A,b)
-  //   xopt = symphonymat(c,intcon,A,b,Aeq,beq)
-  //   xopt = symphonymat(c,intcon,A,b,Aeq,beq,lb,ub)
-  //   xopt = symphonymat(c,intcon,A,b,Aeq,beq,lb,ub,options)
-  //   [xopt,fopt,status,output] = symphonymat( ... )
-  //   
-  //   Parameters
-  //   c : a vector of double, contains coefficients of the variables in the objective 
-  //   intcon : Vector of integer constraints, specified as a vector of positive integers. The values in intcon indicate the components of the decision variable x that are integer-valued. intcon has values from 1 through number of variable.
-  //   A : Linear inequality constraint matrix, specified as a matrix of double. A represents the linear coefficients in the constraints A*x ≤ b. A has the size where columns equals to the number of variables.
-  //   b : Linear inequality constraint vector, specified as a vector of double. b represents the constant vector in the constraints A*x ≤ b. b has size equals to the number of rows in A. 
-  //   Aeq : Linear equality constraint matrix, specified as a matrix of double. Aeq represents the linear coefficients in the constraints Aeq*x = beq. Aeq has the size where columns equals to the number of variables.
-  //   beq : Linear equality constraint vector, specified as a vector of double. beq represents the constant vector in the constraints Aeq*x = beq. beq has size equals to the number of rows in Aeq. 
-  //   lb : Lower bounds, specified as a vector or array of double. lb represents the lower bounds elementwise in lb ≤ x ≤ ub.
-  //   ub : Upper bounds, specified as a vector or array of double. ub represents the upper bounds elementwise in lb ≤ x ≤ ub.
-  //   options : a list containing the the parameters to be set.
-  //   xopt : a vector of double, the computed solution of the optimization problem.
-  //   fopt : a double, the function value at x
-  //   status : status flag from symphony. 227 is optimal, 228 is Time limit exceeded, 230 is iteration limit exceeded.
-  //   output : The output data structure contains detailed information about the optimization process. This version only contains number of iterations.
-  //   
-  //   Description
-  //   Search the minimum or maximum of a constrained mixed integer linear programming optimization problem specified by :
-  //   find the minimum or maximum of C'⋅x such that 
-  //
-  //   
-  //    \begin{eqnarray}
-  //    &\mbox{min}_{x}
-  //    & C^T⋅x \\
-  //    & \text{subject to} & A⋅x \leq b \\
-  //    & & Aeq⋅x = beq \\
-  //    & & lb \leq x \leq ub \\
-  //	& & x_i \in \!\, \mathbb{Z}, i \in \!\, I
-  //    \end{eqnarray}
-  //   
-  //   
-  //   The routine calls SYMPHONY written in C by gateway files for the actual computation.
-  //
-  // Examples
-  //    // Objective function
-  //    c = [350*5,330*3,310*4,280*6,500,450,400,100]';
-  //    // Lower Bound of variable
-  //    lb = repmat(0,1,8);
-  //    // Upper Bound of variables
-  //    ub = [repmat(1,1,4) repmat(%inf,1,4)];
-  //    // Constraint Matrix
-  //    Aeq = [5,3,4,6,1,1,1,1;
-  //                 5*0.05,3*0.04,4*0.05,6*0.03,0.08,0.07,0.06,0.03;
-  //                 5*0.03,3*0.03,4*0.04,6*0.04,0.06,0.07,0.08,0.09;]
-  //    beq = [ 25, 1.25, 1.25]
-  //    intcon = [1 2 3 4];
-  //    // Calling Symphony
-  //    [x,f,status,output] = symphonymat(c,intcon,[],[],Aeq,beq,lb,ub)
-  //	// Press ENTER to continue 
-  //
-  // Examples 
-  //    // An advanced case where we set some options in symphony
-  //    // This problem is taken from 
-  //    // P.C.Chu and J.E.Beasley 
-  //    // "A genetic algorithm for the multidimensional knapsack problem",
-  //    // Journal of Heuristics, vol. 4, 1998, pp63-86.
-  //    // The problem to be solved is:
-  //    // Max  sum{j=1,...,n} p(j)x(j)
-  //    // st   sum{j=1,...,n} r(i,j)x(j) <= b(i)       i=1,...,m
-  //    //                     x(j)=0 or 1
-  //    // The function to be maximize i.e. P(j)
-  //    c = -1*[   504 803 667 1103 834 585 811 856 690 832 846 813 868 793 ..
-  //            825 1002 860 615 540 797 616 660 707 866 647 746 1006 608 ..
-  //            877 900 573 788 484 853 942 630 591 630 640 1169 932 1034 ..
-  //            957 798 669 625 467 1051 552 717 654 388 559 555 1104 783 ..
-  //            959 668 507 855 986 831 821 825 868 852 832 828 799 686 ..
-  //            510 671 575 740 510 675 996 636 826 1022 1140 654 909 799 ..
-  //            1162 653 814 625 599 476 767 954 906 904 649 873 565 853 1008 632]';
-  //    //Constraint Matrix
-  //    A = [   //Constraint 1
-  //            42 41 523 215 819 551 69 193 582 375 367 478 162 898 ..
-  //            550 553 298 577 493 183 260 224 852 394 958 282 402 604 ..
-  //            164 308 218 61 273 772 191 117 276 877 415 873 902 465 ..
-  //            320 870 244 781 86 622 665 155 680 101 665 227 597 354 ..
-  //            597 79 162 998 849 136 112 751 735 884 71 449 266 420 ..
-  //            797 945 746 46 44 545 882 72 383 714 987 183 731 301 ..
-  //            718 91 109 567 708 507 983 808 766 615 554 282 995 946 651 298;
-  //            //Constraint 2
-  //            509 883 229 569 706 639 114 727 491 481 681 948 687 941 ..
-  //            350 253 573 40 124 384 660 951 739 329 146 593 658 816 ..
-  //            638 717 779 289 430 851 937 289 159 260 930 248 656 833 ..
-  //            892 60 278 741 297 967 86 249 354 614 836 290 893 857 ..
-  //            158 869 206 504 799 758 431 580 780 788 583 641 32 653 ..
-  //            252 709 129 368 440 314 287 854 460 594 512 239 719 751 ..
-  //            708 670 269 832 137 356 960 651 398 893 407 477 552 805 881 850;
-  //            //Constraint 3
-  //            806 361 199 781 596 669 957 358 259 888 319 751 275 177 ..
-  //            883 749 229 265 282 694 819 77 190 551 140 442 867 283 ..
-  //            137 359 445 58 440 192 485 744 844 969 50 833 57 877 ..
-  //            482 732 968 113 486 710 439 747 174 260 877 474 841 422 ..
-  //            280 684 330 910 791 322 404 403 519 148 948 414 894 147 ..
-  //            73 297 97 651 380 67 582 973 143 732 624 518 847 113 ..
-  //            382 97 905 398 859 4 142 110 11 213 398 173 106 331 254 447 ;
-  //            //Constraint 4
-  //            404 197 817 1000 44 307 39 659 46 334 448 599 931 776 ..
-  //            263 980 807 378 278 841 700 210 542 636 388 129 203 110 ..
-  //            817 502 657 804 662 989 585 645 113 436 610 948 919 115 ..
-  //            967 13 445 449 740 592 327 167 368 335 179 909 825 614 ..
-  //            987 350 179 415 821 525 774 283 427 275 659 392 73 896 ..
-  //            68 982 697 421 246 672 649 731 191 514 983 886 95 846 ..
-  //            689 206 417 14 735 267 822 977 302 687 118 990 323 993 525 322;
-  //            //Constrain 5
-  //            475 36 287 577 45 700 803 654 196 844 657 387 518 143 ..
-  //            515 335 942 701 332 803 265 922 908 139 995 845 487 100 ..
-  //            447 653 649 738 424 475 425 926 795 47 136 801 904 740 ..
-  //            768 460 76 660 500 915 897 25 716 557 72 696 653 933 ..
-  //            420 582 810 861 758 647 237 631 271 91 75 756 409 440 ..
-  //            483 336 765 637 981 980 202 35 594 689 602 76 767 693 ..
-  //            893 160 785 311 417 748 375 362 617 553 474 915 457 261 350 635 ;
-  //     ];
-  //    nbVar = size(c,1)
-  //    b=[11927 13727 11551 13056 13460 ];
-  //    // Lower Bound of variables
-  //    lb = repmat(0,1,nbVar)
-  //    // Upper Bound of variables
-  //    ub = repmat(1,1,nbVar)
-  //    // Lower Bound of constrains
-  //    intcon = [];
-  //    for i = 1:nbVar
-  //        intcon = [intcon i];
-  //    end
-  //    options = list("time_limit", 25);
-  //    // The expected solution :
-  //    // Output variables
-  //    xopt = [0 1 1 0 0 1 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 1 0 1 1 0 1 ..
-  //            0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 1 ..
-  //            0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 1 0 0 0 0 0 1 1 0 0 0 0 0 1 1 0 0 1 0 0 1 0]
-  //    // Optimal value
-  //    fopt = [ 24381 ]
-  //    // Calling Symphony
-  //    [x,f,status,output] = symphonymat(c,intcon,A,b,[],[],lb,ub,options);
-  // Authors
-  // Keyur Joshi, Saikiran, Iswarya, Harpreet Singh
+	// Solves a mixed integer linear programming constrained optimization problem in intlinprog format.
+	//
+	//   Calling Sequence
+	//   xopt = symphonymat(c,intcon,A,b)
+	//   xopt = symphonymat(c,intcon,A,b,Aeq,beq)
+	//   xopt = symphonymat(c,intcon,A,b,Aeq,beq,lb,ub)
+	//   xopt = symphonymat(c,intcon,A,b,Aeq,beq,lb,ub,options)
+	//   [xopt,fopt,status,output] = symphonymat( ... )
+	//   
+	//   Parameters
+	//   c : a vector of double, contains coefficients of the variables in the objective 
+	//   intcon : Vector of integer constraints, specified as a vector of positive integers. The values in intcon indicate the components of the decision variable x that are integer-valued. intcon has values from 1 through number of variable.
+	//   A : Linear inequality constraint matrix, specified as a matrix of double. A represents the linear coefficients in the constraints A*x ≤ b. A has the size where columns equals to the number of variables.
+	//   b : Linear inequality constraint vector, specified as a vector of double. b represents the constant vector in the constraints A*x ≤ b. b has size equals to the number of rows in A. 
+	//   Aeq : Linear equality constraint matrix, specified as a matrix of double. Aeq represents the linear coefficients in the constraints Aeq*x = beq. Aeq has the size where columns equals to the number of variables.
+	//   beq : Linear equality constraint vector, specified as a vector of double. beq represents the constant vector in the constraints Aeq*x = beq. beq has size equals to the number of rows in Aeq. 
+	//   lb : Lower bounds, specified as a vector or array of double. lb represents the lower bounds elementwise in lb ≤ x ≤ ub.
+	//   ub : Upper bounds, specified as a vector or array of double. ub represents the upper bounds elementwise in lb ≤ x ≤ ub.
+	//   options : a list containing the the parameters to be set.
+	//   xopt : a vector of double, the computed solution of the optimization problem.
+	//   fopt : a double, the function value at x
+	//   status : status flag returned from symphony. 227 is optimal, 228 is Time limit exceeded, 230 is iteration limit exceeded.
+	//   output : The output data structure contains detailed information about the optimization process. This version only contains number of iterations.
+	//   
+	//   Description
+	//   Search the minimum or maximum of a constrained mixed integer linear programming optimization problem specified by :
+	//
+	//   
+	//    \begin{eqnarray}
+	//    &\mbox{min}_{x}
+	//    & C^T⋅x \\
+	//    & \text{subject to} & A⋅x \leq b \\
+	//    & & Aeq⋅x = beq \\
+	//    & & lb \leq x \leq ub \\
+	//	& & x_i \in \!\, \mathbb{Z}, i \in \!\, I
+	//    \end{eqnarray}
+	//   
+	//   
+	//   The routine calls SYMPHONY written in C by gateway files for the actual computation.
+	//
+	// Examples
+	//    // Objective function
+	// 	  // Reference: Westerberg, Carl-Henrik, Bengt Bjorklund, and Eskil Hultman. "An application of mixed integer programming in a Swedish steel mill." Interfaces 7, no. 2 (1977): 39-43.
+	//    c = [350*5,330*3,310*4,280*6,500,450,400,100]';
+	//    // Lower Bound of variable
+	//    lb = repmat(0,1,8);
+	//    // Upper Bound of variables
+	//    ub = [repmat(1,1,4) repmat(%inf,1,4)];
+	//    // Constraint Matrix
+	//    Aeq = [5,3,4,6,1,1,1,1;
+	//                 5*0.05,3*0.04,4*0.05,6*0.03,0.08,0.07,0.06,0.03;
+	//                 5*0.03,3*0.03,4*0.04,6*0.04,0.06,0.07,0.08,0.09;]
+	//    beq = [ 25, 1.25, 1.25]
+	//    intcon = [1 2 3 4];
+	//    // Calling Symphony
+	//    [x,f,status,output] = symphonymat(c,intcon,[],[],Aeq,beq,lb,ub)
+	//	// Press ENTER to continue 
+	//
+	// Examples 
+	//    // An advanced case where we set some options in symphony
+	//    // This problem is taken from 
+	//    // P.C.Chu and J.E.Beasley 
+	//    // "A genetic algorithm for the multidimensional knapsack problem",
+	//    // Journal of Heuristics, vol. 4, 1998, pp63-86.
+	//    // The problem to be solved is:
+	//    // Max  sum{j=1,...,n} p(j)x(j)
+	//    // st   sum{j=1,...,n} r(i,j)x(j) <= b(i)       i=1,...,m
+	//    //                     x(j)=0 or 1
+	//    // The function to be maximize i.e. P(j)
+	//    c = -1*[   504 803 667 1103 834 585 811 856 690 832 846 813 868 793 ..
+	//            825 1002 860 615 540 797 616 660 707 866 647 746 1006 608 ..
+	//            877 900 573 788 484 853 942 630 591 630 640 1169 932 1034 ..
+	//            957 798 669 625 467 1051 552 717 654 388 559 555 1104 783 ..
+	//            959 668 507 855 986 831 821 825 868 852 832 828 799 686 ..
+	//            510 671 575 740 510 675 996 636 826 1022 1140 654 909 799 ..
+	//            1162 653 814 625 599 476 767 954 906 904 649 873 565 853 1008 632]';
+	//    //Constraint Matrix
+	//    A = [   //Constraint 1
+	//            42 41 523 215 819 551 69 193 582 375 367 478 162 898 ..
+	//            550 553 298 577 493 183 260 224 852 394 958 282 402 604 ..
+	//            164 308 218 61 273 772 191 117 276 877 415 873 902 465 ..
+	//            320 870 244 781 86 622 665 155 680 101 665 227 597 354 ..
+	//            597 79 162 998 849 136 112 751 735 884 71 449 266 420 ..
+	//            797 945 746 46 44 545 882 72 383 714 987 183 731 301 ..
+	//            718 91 109 567 708 507 983 808 766 615 554 282 995 946 651 298;
+	//            //Constraint 2
+	//            509 883 229 569 706 639 114 727 491 481 681 948 687 941 ..
+	//            350 253 573 40 124 384 660 951 739 329 146 593 658 816 ..
+	//            638 717 779 289 430 851 937 289 159 260 930 248 656 833 ..
+	//            892 60 278 741 297 967 86 249 354 614 836 290 893 857 ..
+	//            158 869 206 504 799 758 431 580 780 788 583 641 32 653 ..
+	//            252 709 129 368 440 314 287 854 460 594 512 239 719 751 ..
+	//            708 670 269 832 137 356 960 651 398 893 407 477 552 805 881 850;
+	//            //Constraint 3
+	//            806 361 199 781 596 669 957 358 259 888 319 751 275 177 ..
+	//            883 749 229 265 282 694 819 77 190 551 140 442 867 283 ..
+	//            137 359 445 58 440 192 485 744 844 969 50 833 57 877 ..
+	//            482 732 968 113 486 710 439 747 174 260 877 474 841 422 ..
+	//            280 684 330 910 791 322 404 403 519 148 948 414 894 147 ..
+	//            73 297 97 651 380 67 582 973 143 732 624 518 847 113 ..
+	//            382 97 905 398 859 4 142 110 11 213 398 173 106 331 254 447 ;
+	//            //Constraint 4
+	//            404 197 817 1000 44 307 39 659 46 334 448 599 931 776 ..
+	//            263 980 807 378 278 841 700 210 542 636 388 129 203 110 ..
+	//            817 502 657 804 662 989 585 645 113 436 610 948 919 115 ..
+	//            967 13 445 449 740 592 327 167 368 335 179 909 825 614 ..
+	//            987 350 179 415 821 525 774 283 427 275 659 392 73 896 ..
+	//            68 982 697 421 246 672 649 731 191 514 983 886 95 846 ..
+	//            689 206 417 14 735 267 822 977 302 687 118 990 323 993 525 322;
+	//            //Constrain 5
+	//            475 36 287 577 45 700 803 654 196 844 657 387 518 143 ..
+	//            515 335 942 701 332 803 265 922 908 139 995 845 487 100 ..
+	//            447 653 649 738 424 475 425 926 795 47 136 801 904 740 ..
+	//            768 460 76 660 500 915 897 25 716 557 72 696 653 933 ..
+	//            420 582 810 861 758 647 237 631 271 91 75 756 409 440 ..
+	//            483 336 765 637 981 980 202 35 594 689 602 76 767 693 ..
+	//            893 160 785 311 417 748 375 362 617 553 474 915 457 261 350 635 ;
+	//     ];
+	//    nbVar = size(c,1)
+	//    b=[11927 13727 11551 13056 13460 ];
+	//    // Lower Bound of variables
+	//    lb = repmat(0,1,nbVar)
+	//    // Upper Bound of variables
+	//    ub = repmat(1,1,nbVar)
+	//    // Lower Bound of constrains
+	//    intcon = [];
+	//    for i = 1:nbVar
+	//        intcon = [intcon i];
+	//    end
+	//    options = list("time_limit", 25);
+	//    // The expected solution :
+	//    // Output variables
+	//    xopt = [0 1 1 0 0 1 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 1 0 1 1 0 1 ..
+	//            0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 1 ..
+	//            0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 1 0 0 0 0 0 1 1 0 0 0 0 0 1 1 0 0 1 0 0 1 0]
+	//    // Optimal value
+	//    fopt = [ 24381 ]
+	//    // Calling Symphony
+	//    [x,f,status,output] = symphonymat(c,intcon,A,b,[],[],lb,ub,options);
+	// Authors
+	// Keyur Joshi, Saikiran, Iswarya, Harpreet Singh
     
     
-//To check the number of input and output argument
-   [lhs , rhs] = argn();
+	//To check the number of input and output argument
+   	[lhs , rhs] = argn();
 	
-//To check the number of argument given by user
+	//To check the number of argument given by user
 	if ( rhs < 4 | rhs == 5 | rhs == 7 | rhs > 9 ) then
 		errmsg = msprintf(gettext("%s: Unexpected number of input arguments : %d provided while should be in the set [4 6 8 9]"), "Symphony", rhs);
 		error(errmsg);
@@ -171,7 +171,6 @@ function [xopt,fopt,status,iter] = symphonymat (varargin)
 	lb = [];
 	ub = [];
 	
-	
 	c = varargin(1)
 	intcon = varargin(2)
 	A = varargin(3)
diff --git a/sci_gateway/cpp/.sci_QuadNLP.cpp.swp b/sci_gateway/cpp/.sci_QuadNLP.cpp.swp
new file mode 100644
index 0000000..764077c
Binary files /dev/null and b/sci_gateway/cpp/.sci_QuadNLP.cpp.swp differ
diff --git a/sci_gateway/cpp/QuadNLP.hpp b/sci_gateway/cpp/QuadNLP.hpp
index ec40195..4020913 100644
--- a/sci_gateway/cpp/QuadNLP.hpp
+++ b/sci_gateway/cpp/QuadNLP.hpp
@@ -1,14 +1,13 @@
-/*
- * Quadratic Programming Toolbox for Scilab using IPOPT library
- * Authors :
-	Sai Kiran
-	Keyur Joshi
-	Iswarya
-
-
- * Optimizing (minimizing) the quadratic objective function having any number of variables and linear constraints.
- *
-*/
+// Copyright (C) 2015 - IIT Bombay - FOSSEE
+//
+// This file must be used under the terms of the CeCILL.
+// This source file is licensed as described in the file COPYING, which
+// you should have received as part of this distribution.  The terms
+// are also available at
+// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt
+// Author: Harpreet Singh, Sai Kiran, Keyur Joshi, Iswarya
+// Organization: FOSSEE, IIT Bombay
+// Email: toolbox@scilab.in
 
 #ifndef __QuadNLP_HPP__
 #define __QuadNLP_HPP__
diff --git a/sci_gateway/cpp/builder_gateway_cpp.sce b/sci_gateway/cpp/builder_gateway_cpp.sce
index 225edd8..6e4adf7 100644
--- a/sci_gateway/cpp/builder_gateway_cpp.sce
+++ b/sci_gateway/cpp/builder_gateway_cpp.sce
@@ -1,13 +1,13 @@
 // Copyright (C) 2015 - IIT Bombay - FOSSEE
 //
-// Author: Keyur Joshi, Sai Kiran, Iswarya and Harpreet Singh
-// Organization: FOSSEE, IIT Bombay
-// Email: harpreet.mertia@gmail.com
 // This file must be used under the terms of the CeCILL.
 // This source file is licensed as described in the file COPYING, which
 // you should have received as part of this distribution.  The terms
 // are also available at
 // http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt
+// Author: Harpreet Singh
+// Organization: FOSSEE, IIT Bombay
+// Email: toolbox@scilab.in
 
 mode(-1)
 lines(0)
diff --git a/sci_gateway/cpp/globals.cpp b/sci_gateway/cpp/globals.cpp
index 4b5d99c..92767bc 100644
--- a/sci_gateway/cpp/globals.cpp
+++ b/sci_gateway/cpp/globals.cpp
@@ -1,9 +1,14 @@
-/*
- * Implementation Symphony Tool Box for Scilab
- * globals.cpp
- * contains definitions of global variables and functions
- * By Keyur Joshi and Sai Kiran
- */
+// Copyright (C) 2015 - IIT Bombay - FOSSEE
+//
+// This file must be used under the terms of the CeCILL.
+// This source file is licensed as described in the file COPYING, which
+// you should have received as part of this distribution.  The terms
+// are also available at
+// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt
+// Author: Keyur Joshi and Sai Kiran
+// Organization: FOSSEE, IIT Bombay
+// Email: toolbox@scilab.in
+
 #include 
 #include 
 
diff --git a/sci_gateway/cpp/libsymphonytools.c b/sci_gateway/cpp/libsymphonytools.c
deleted file mode 100644
index 1007284..0000000
--- a/sci_gateway/cpp/libsymphonytools.c
+++ /dev/null
@@ -1,144 +0,0 @@
-#ifdef __cplusplus
-extern "C" {
-#endif
-#include  
-#include 
-#include 
-#include 
-static int direct_gateway(char *fname,void F(void)) { F();return 0;};
-extern Gatefunc sci_sym_open;
-extern Gatefunc sci_sym_close;
-extern Gatefunc sci_sym_isEnvActive;
-extern Gatefunc sci_sym_set_defaults;
-extern Gatefunc sci_sym_set_int_param;
-extern Gatefunc sci_sym_get_int_param;
-extern Gatefunc sci_sym_set_dbl_param;
-extern Gatefunc sci_sym_get_dbl_param;
-extern Gatefunc sci_sym_set_str_param;
-extern Gatefunc sci_sym_get_str_param;
-extern Gatefunc sci_sym_getInfinity;
-extern Gatefunc sci_sym_loadProblemBasic;
-extern Gatefunc sci_sym_loadProblem;
-extern Gatefunc sci_sym_load_mps;
-extern Gatefunc sci_sym_get_num_int;
-extern Gatefunc sci_sym_get_num_int;
-extern Gatefunc sci_sym_get_num_int;
-extern Gatefunc sci_sym_isContinuous;
-extern Gatefunc sci_sym_isBinary;
-extern Gatefunc sci_sym_isInteger;
-extern Gatefunc sci_sym_set_continuous;
-extern Gatefunc sci_sym_set_integer;
-extern Gatefunc sci_sym_get_dbl_arr;
-extern Gatefunc sci_sym_get_dbl_arr;
-extern Gatefunc sci_sym_setVarBound;
-extern Gatefunc sci_sym_setVarBound;
-extern Gatefunc sci_sym_get_dbl_arr;
-extern Gatefunc sci_sym_setObjCoeff;
-extern Gatefunc sci_sym_getObjSense;
-extern Gatefunc sci_sym_setObjSense;
-extern Gatefunc sci_sym_get_dbl_arr;
-extern Gatefunc sci_sym_get_dbl_arr;
-extern Gatefunc sci_sym_get_dbl_arr;
-extern Gatefunc sci_sym_get_dbl_arr;
-extern Gatefunc sci_sym_setConstrBound;
-extern Gatefunc sci_sym_setConstrBound;
-extern Gatefunc sci_sym_setConstrType;
-extern Gatefunc sci_sym_get_matrix;
-extern Gatefunc sci_sym_get_row_sense;
-extern Gatefunc sci_sym_addConstr;
-extern Gatefunc sci_sym_addVar;
-extern Gatefunc sci_sym_delete_cols;
-extern Gatefunc sci_sym_delete_rows;
-extern Gatefunc sci_sym_getPrimalBound;
-extern Gatefunc sci_sym_setPrimalBound;
-extern Gatefunc sci_sym_setColSoln;
-extern Gatefunc sci_sym_solve;
-extern Gatefunc sci_sym_get_status;
-extern Gatefunc sci_sym_get_solver_status;
-extern Gatefunc sci_sym_get_solver_status;
-extern Gatefunc sci_sym_get_solver_status;
-extern Gatefunc sci_sym_get_solver_status;
-extern Gatefunc sci_sym_get_solver_status;
-extern Gatefunc sci_sym_get_solver_status;
-extern Gatefunc sci_sym_getVarSoln;
-extern Gatefunc sci_sym_getObjVal;
-extern Gatefunc sci_sym_get_iteration_count;
-extern Gatefunc sci_sym_getRowActivity;
-static GenericTable Tab[]={
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_open,"sym_open"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_close,"sym_close"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_isEnvActive,"sym_isEnvActive"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_set_defaults,"sym_resetParams"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_set_int_param,"sym_setIntParam"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_get_int_param,"sym_getIntParam"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_set_dbl_param,"sym_setDblParam"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_get_dbl_param,"sym_getDblParam"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_set_str_param,"sym_setStrParam"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_get_str_param,"sym_getStrParam"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_getInfinity,"sym_getInfinity"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_loadProblemBasic,"sym_loadProblemBasic"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_loadProblem,"sym_loadProblem"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_load_mps,"sym_loadMPS"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_get_num_int,"sym_getNumConstr"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_get_num_int,"sym_getNumVar"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_get_num_int,"sym_getNumElements"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_isContinuous,"sym_isContinuous"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_isBinary,"sym_isBinary"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_isInteger,"sym_isInteger"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_set_continuous,"sym_setContinuous"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_set_integer,"sym_setInteger"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_get_dbl_arr,"sym_getVarLower"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_get_dbl_arr,"sym_getVarUpper"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_setVarBound,"sym_setVarLower"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_setVarBound,"sym_setVarUpper"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_get_dbl_arr,"sym_getObjCoeff"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_setObjCoeff,"sym_setObjCoeff"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_getObjSense,"sym_getObjSense"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_setObjSense,"sym_setObjSense"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_get_dbl_arr,"sym_getRhs"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_get_dbl_arr,"sym_getConstrRange"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_get_dbl_arr,"sym_getConstrLower"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_get_dbl_arr,"sym_getConstrUpper"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_setConstrBound,"sym_setConstrLower"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_setConstrBound,"sym_setConstrUpper"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_setConstrType,"sym_setConstrType"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_get_matrix,"sym_getMatrix"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_get_row_sense,"sym_getConstrSense"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_addConstr,"sym_addConstr"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_addVar,"sym_addVar"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_delete_cols,"sym_deleteVars"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_delete_rows,"sym_deleteConstrs"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_getPrimalBound,"sym_getPrimalBound"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_setPrimalBound,"sym_setPrimalBound"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_setColSoln,"sym_setVarSoln"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_solve,"sym_solve"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_get_status,"sym_getStatus"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_get_solver_status,"sym_isOptimal"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_get_solver_status,"sym_isInfeasible"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_get_solver_status,"sym_isAbandoned"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_get_solver_status,"sym_isIterLimitReached"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_get_solver_status,"sym_isTimeLimitReached"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_get_solver_status,"sym_isTargetGapAchieved"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_getVarSoln,"sym_getVarSoln"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_getObjVal,"sym_getObjVal"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_get_iteration_count,"sym_getIterCount"},
-  {(Myinterfun)sci_gateway_without_putlhsvar,sci_sym_getRowActivity,"sym_getConstrActivity"},
-};
- 
-int C2F(libsymphonytools)()
-{
-  Rhs = Max(0, Rhs);
-  if (*(Tab[Fin-1].f) != NULL) 
-  {
-     if(pvApiCtx == NULL)
-     {
-       pvApiCtx = (StrCtx*)MALLOC(sizeof(StrCtx));
-     }
-     pvApiCtx->pstName = (char*)Tab[Fin-1].name;
-    (*(Tab[Fin-1].f))(Tab[Fin-1].name,Tab[Fin-1].F);
-  }
-  return 0;
-}
-#ifdef __cplusplus
-}
-#endif
diff --git a/sci_gateway/cpp/libsymphonytools.so b/sci_gateway/cpp/libsymphonytools.so
deleted file mode 100755
index 31e63b1..0000000
Binary files a/sci_gateway/cpp/libsymphonytools.so and /dev/null differ
diff --git a/sci_gateway/cpp/sci_QuadNLP.cpp b/sci_gateway/cpp/sci_QuadNLP.cpp
index 50e8109..b1a0e04 100644
--- a/sci_gateway/cpp/sci_QuadNLP.cpp
+++ b/sci_gateway/cpp/sci_QuadNLP.cpp
@@ -1,10 +1,13 @@
-/*
- * Quadratic Programming Toolbox for Scilab using IPOPT library
- * Authors :
-	Sai Kiran
-	Keyur Joshi
-	Iswarya
- */
+// Copyright (C) 2015 - IIT Bombay - FOSSEE
+//
+// This file must be used under the terms of the CeCILL.
+// This source file is licensed as described in the file COPYING, which
+// you should have received as part of this distribution.  The terms
+// are also available at
+// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt
+// Author: Harpreet Singh, Sai Kiran, Keyur Joshi, Iswarya
+// Organization: FOSSEE, IIT Bombay
+// Email: toolbox@scilab.in
 
 #include "QuadNLP.hpp"
 #include "IpIpoptData.hpp"
diff --git a/sci_gateway/cpp/sci_iofunc.cpp b/sci_gateway/cpp/sci_iofunc.cpp
index 04b5639..e92c318 100644
--- a/sci_gateway/cpp/sci_iofunc.cpp
+++ b/sci_gateway/cpp/sci_iofunc.cpp
@@ -1,6 +1,14 @@
-// Symphony Toolbox for Scilab
-// (Definition of) Functions for input and output from Scilab
-// By Keyur Joshi
+// Copyright (C) 2015 - IIT Bombay - FOSSEE
+//
+// This file must be used under the terms of the CeCILL.
+// This source file is licensed as described in the file COPYING, which
+// you should have received as part of this distribution.  The terms
+// are also available at
+// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt
+// Author: Keyur Joshi, Harpreet Singh
+// Organization: FOSSEE, IIT Bombay
+// Email: toolbox@scilab.in
+
 
 #include "api_scilab.h"
 #include "Scierror.h"
diff --git a/sci_gateway/cpp/sci_iofunc.hpp b/sci_gateway/cpp/sci_iofunc.hpp
index 3833777..fc379f4 100644
--- a/sci_gateway/cpp/sci_iofunc.hpp
+++ b/sci_gateway/cpp/sci_iofunc.hpp
@@ -1,6 +1,13 @@
-// Symphony Toolbox for Scilab
-// (Declaration of) Functions for input and output from Scilab
-// By Keyur Joshi
+// Copyright (C) 2015 - IIT Bombay - FOSSEE
+//
+// This file must be used under the terms of the CeCILL.
+// This source file is licensed as described in the file COPYING, which
+// you should have received as part of this distribution.  The terms
+// are also available at
+// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt
+// Author: Keyur Joshi, Harpreet Singh
+// Organization: FOSSEE, IIT Bombay
+// Email: toolbox@scilab.in
 
 #ifndef SCI_IOFUNCHEADER
 #define SCI_IOFUNCHEADER
diff --git a/sci_gateway/cpp/sci_ipopt.cpp b/sci_gateway/cpp/sci_ipopt.cpp
index 7094615..cbb71df 100644
--- a/sci_gateway/cpp/sci_ipopt.cpp
+++ b/sci_gateway/cpp/sci_ipopt.cpp
@@ -1,11 +1,13 @@
-/*
- * Quadratic Programming Toolbox for Scilab using IPOPT library
- * Authors :
-	Sai Kiran
-	Keyur Joshi
-	Iswarya
-	Harpreet Singh
- */
+// Copyright (C) 2015 - IIT Bombay - FOSSEE
+//
+// This file must be used under the terms of the CeCILL.
+// This source file is licensed as described in the file COPYING, which
+// you should have received as part of this distribution.  The terms
+// are also available at
+// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt
+// Author: Harpreet Singh, Sai Kiran, Keyur Joshi, Iswarya
+// Organization: FOSSEE, IIT Bombay
+// Email: toolbox@scilab.in
 
 #include "sci_iofunc.hpp"
 #include "IpIpoptApplication.hpp"
diff --git a/sci_gateway/cpp/sci_solver_status_query_functions.cpp b/sci_gateway/cpp/sci_solver_status_query_functions.cpp
index eb64bd1..2927717 100644
--- a/sci_gateway/cpp/sci_solver_status_query_functions.cpp
+++ b/sci_gateway/cpp/sci_solver_status_query_functions.cpp
@@ -1,9 +1,13 @@
-/*
- * Implementation of Symphony Tool Box for Scilab
- * solver_status_query_functions.cpp
- * contains Solver Status Query Functions (7 functions)
- * Author: Sai Kiran
- */
+// Copyright (C) 2015 - IIT Bombay - FOSSEE
+//
+// This file must be used under the terms of the CeCILL.
+// This source file is licensed as described in the file COPYING, which
+// you should have received as part of this distribution.  The terms
+// are also available at
+// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt
+// Author: Sai Kiran
+// Organization: FOSSEE, IIT Bombay
+// Email: toolbox@scilab.in
 
 #include 
 #include 
diff --git a/sci_gateway/cpp/sci_sym_addrowcol.cpp b/sci_gateway/cpp/sci_sym_addrowcol.cpp
index 4e1d76e..7c852f9 100644
--- a/sci_gateway/cpp/sci_sym_addrowcol.cpp
+++ b/sci_gateway/cpp/sci_sym_addrowcol.cpp
@@ -1,8 +1,14 @@
-/*
- * Symphony Toolbox
- * Functions to add a new row or column
- * By Keyur Joshi
- */
+// Copyright (C) 2015 - IIT Bombay - FOSSEE
+//
+// This file must be used under the terms of the CeCILL.
+// This source file is licensed as described in the file COPYING, which
+// you should have received as part of this distribution.  The terms
+// are also available at
+// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt
+// Author: Keyur Joshi
+// Organization: FOSSEE, IIT Bombay
+// Email: toolbox@scilab.in
+
 #include "symphony.h"
 #include "sci_iofunc.hpp"
 
diff --git a/sci_gateway/cpp/sci_sym_getinfinity.cpp b/sci_gateway/cpp/sci_sym_getinfinity.cpp
index c7fe243..20937d0 100644
--- a/sci_gateway/cpp/sci_sym_getinfinity.cpp
+++ b/sci_gateway/cpp/sci_sym_getinfinity.cpp
@@ -1,8 +1,14 @@
-/*
- * Symphony Toolbox
- * Provides the Symphony infinity value
- * By Keyur Joshi
- */
+// Copyright (C) 2015 - IIT Bombay - FOSSEE
+//
+// This file must be used under the terms of the CeCILL.
+// This source file is licensed as described in the file COPYING, which
+// you should have received as part of this distribution.  The terms
+// are also available at
+// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt
+// Author: Keyur Joshi
+// Organization: FOSSEE, IIT Bombay
+// Email: toolbox@scilab.in
+
 #include "symphony.h"
 #include "sci_iofunc.hpp"
 
diff --git a/sci_gateway/cpp/sci_sym_getobjsense.cpp b/sci_gateway/cpp/sci_sym_getobjsense.cpp
index 3df325c..23b9291 100644
--- a/sci_gateway/cpp/sci_sym_getobjsense.cpp
+++ b/sci_gateway/cpp/sci_sym_getobjsense.cpp
@@ -1,8 +1,14 @@
-/*
- * Symphony Toolbox
- * 
- * 
- */
+// Copyright (C) 2015 - IIT Bombay - FOSSEE
+//
+// This file must be used under the terms of the CeCILL.
+// This source file is licensed as described in the file COPYING, which
+// you should have received as part of this distribution.  The terms
+// are also available at
+// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt
+// Author: Sai Kiran, Keyur Joshi, Iswarya
+// Organization: FOSSEE, IIT Bombay
+// Email: toolbox@scilab.in
+
 #include "symphony.h"
 #include "sci_iofunc.hpp"
 
diff --git a/sci_gateway/cpp/sci_sym_getrowact.cpp b/sci_gateway/cpp/sci_sym_getrowact.cpp
index ebfd9ff..63b9e15 100644
--- a/sci_gateway/cpp/sci_sym_getrowact.cpp
+++ b/sci_gateway/cpp/sci_sym_getrowact.cpp
@@ -1,8 +1,14 @@
-/*
- * Symphony Toolbox
- * Function to get the row activity after solving
- * By Keyur Joshi
- */
+// Copyright (C) 2015 - IIT Bombay - FOSSEE
+//
+// This file must be used under the terms of the CeCILL.
+// This source file is licensed as described in the file COPYING, which
+// you should have received as part of this distribution.  The terms
+// are also available at
+// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt
+// Author: Keyur Joshi
+// Organization: FOSSEE, IIT Bombay
+// Email: toolbox@scilab.in
+
 #include "symphony.h"
 #include "sci_iofunc.hpp"
 
diff --git a/sci_gateway/cpp/sci_sym_isenvactive.cpp b/sci_gateway/cpp/sci_sym_isenvactive.cpp
index 96d5976..a0e3f55 100644
--- a/sci_gateway/cpp/sci_sym_isenvactive.cpp
+++ b/sci_gateway/cpp/sci_sym_isenvactive.cpp
@@ -1,8 +1,14 @@
-/*
- * Symphony Toolbox
- * Check if Symphony environment is active
- * Made by Keyur Joshi
- */
+// Copyright (C) 2015 - IIT Bombay - FOSSEE
+//
+// This file must be used under the terms of the CeCILL.
+// This source file is licensed as described in the file COPYING, which
+// you should have received as part of this distribution.  The terms
+// are also available at
+// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt
+// Author: Keyur Joshi
+// Organization: FOSSEE, IIT Bombay
+// Email: toolbox@scilab.in
+
 #include "symphony.h"
 #include "sci_iofunc.hpp"
 
diff --git a/sci_gateway/cpp/sci_sym_load_mps.cpp b/sci_gateway/cpp/sci_sym_load_mps.cpp
index 7f000fb..7c87b8b 100644
--- a/sci_gateway/cpp/sci_sym_load_mps.cpp
+++ b/sci_gateway/cpp/sci_sym_load_mps.cpp
@@ -1,9 +1,14 @@
-/*
- * Implementation Symphony Tool Box for Scilab
- * template.cpp
- * contains function for loading .mps file to symphony
- * By Iswarya
- */
+// Copyright (C) 2015 - IIT Bombay - FOSSEE
+//
+// This file must be used under the terms of the CeCILL.
+// This source file is licensed as described in the file COPYING, which
+// you should have received as part of this distribution.  The terms
+// are also available at
+// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt
+// Author: Iswarya
+// Organization: FOSSEE, IIT Bombay
+// Email: toolbox@scilab.in
+
 #include 
 #include "sci_iofunc.hpp"
 
diff --git a/sci_gateway/cpp/sci_sym_loadproblem.cpp b/sci_gateway/cpp/sci_sym_loadproblem.cpp
index b732eeb..5294082 100644
--- a/sci_gateway/cpp/sci_sym_loadproblem.cpp
+++ b/sci_gateway/cpp/sci_sym_loadproblem.cpp
@@ -1,8 +1,15 @@
-/*
- * Symphony Toolbox
- * Explicit problem loaders
- * Made by Keyur Joshi
- */
+
+// Copyright (C) 2015 - IIT Bombay - FOSSEE
+//
+// This file must be used under the terms of the CeCILL.
+// This source file is licensed as described in the file COPYING, which
+// you should have received as part of this distribution.  The terms
+// are also available at
+// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt
+// Author: Keyur Joshi
+// Organization: FOSSEE, IIT Bombay
+// Email: toolbox@scilab.in
+
 #include "symphony.h"
 #include "sci_iofunc.hpp"
 
diff --git a/sci_gateway/cpp/sci_sym_openclose.cpp b/sci_gateway/cpp/sci_sym_openclose.cpp
index 4bab3e1..f77a98b 100644
--- a/sci_gateway/cpp/sci_sym_openclose.cpp
+++ b/sci_gateway/cpp/sci_sym_openclose.cpp
@@ -1,8 +1,14 @@
-/*
- * Symphony Tool Box for Scilab
- * contains functions that opens and closes the symphony environment 
- * By Keyur Joshi, Iswarya
- */
+// Copyright (C) 2015 - IIT Bombay - FOSSEE
+//
+// This file must be used under the terms of the CeCILL.
+// This source file is licensed as described in the file COPYING, which
+// you should have received as part of this distribution.  The terms
+// are also available at
+// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt
+// Author: Keyur Joshi, Iswarya
+// Organization: FOSSEE, IIT Bombay
+// Email: toolbox@scilab.in
+
 #include 
 #include "sci_iofunc.hpp"
 
diff --git a/sci_gateway/cpp/sci_sym_primalbound.cpp b/sci_gateway/cpp/sci_sym_primalbound.cpp
index 7e79d87..d5b4d3f 100644
--- a/sci_gateway/cpp/sci_sym_primalbound.cpp
+++ b/sci_gateway/cpp/sci_sym_primalbound.cpp
@@ -1,8 +1,14 @@
-/*
- * Symphony Toolbox
- * Functions for getting/setting the primal bound
- * By Keyur Joshi
- */
+// Copyright (C) 2015 - IIT Bombay - FOSSEE
+//
+// This file must be used under the terms of the CeCILL.
+// This source file is licensed as described in the file COPYING, which
+// you should have received as part of this distribution.  The terms
+// are also available at
+// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt
+// Author: Keyur Joshi
+// Organization: FOSSEE, IIT Bombay
+// Email: toolbox@scilab.in
+
 #include "symphony.h"
 #include "sci_iofunc.hpp"
 
diff --git a/sci_gateway/cpp/sci_sym_remove.cpp b/sci_gateway/cpp/sci_sym_remove.cpp
index be9c72b..0289064 100644
--- a/sci_gateway/cpp/sci_sym_remove.cpp
+++ b/sci_gateway/cpp/sci_sym_remove.cpp
@@ -1,9 +1,14 @@
-/*
- * Implementation Symphony Tool Box for Scilab
- * set_sym_remove.cpp
- * contains function for removing columns and rows
- * By Iswarya
- */
+// Copyright (C) 2015 - IIT Bombay - FOSSEE
+//
+// This file must be used under the terms of the CeCILL.
+// This source file is licensed as described in the file COPYING, which
+// you should have received as part of this distribution.  The terms
+// are also available at
+// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt
+// Author: Iswarya
+// Organization: FOSSEE, IIT Bombay
+// Email: toolbox@scilab.in
+
 #include 
 
 extern sym_environment* global_sym_env;//defined in globals.cpp
diff --git a/sci_gateway/cpp/sci_sym_rowmod.cpp b/sci_gateway/cpp/sci_sym_rowmod.cpp
index ec966d0..3819d4e 100644
--- a/sci_gateway/cpp/sci_sym_rowmod.cpp
+++ b/sci_gateway/cpp/sci_sym_rowmod.cpp
@@ -1,8 +1,14 @@
-/*
- * Symphony Toolbox
- * Functions for modifying constraints
- * By Keyur Joshi
- */
+// Copyright (C) 2015 - IIT Bombay - FOSSEE
+//
+// This file must be used under the terms of the CeCILL.
+// This source file is licensed as described in the file COPYING, which
+// you should have received as part of this distribution.  The terms
+// are also available at
+// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt
+// Author: Keyur Joshi
+// Organization: FOSSEE, IIT Bombay
+// Email: toolbox@scilab.in
+
 #include "symphony.h"
 #include "sci_iofunc.hpp"
 
diff --git a/sci_gateway/cpp/sci_sym_set_indices.cpp b/sci_gateway/cpp/sci_sym_set_indices.cpp
index 6b8a35a..4e1577d 100644
--- a/sci_gateway/cpp/sci_sym_set_indices.cpp
+++ b/sci_gateway/cpp/sci_sym_set_indices.cpp
@@ -1,9 +1,14 @@
-/*
- * Implementation Symphony Tool Box for Scilab
- * sci_sym_set_indices.cpp
- * contains functions for setting index variables as continuous and integer values
- * By Iswarya
- */
+// Copyright (C) 2015 - IIT Bombay - FOSSEE
+//
+// This file must be used under the terms of the CeCILL.
+// This source file is licensed as described in the file COPYING, which
+// you should have received as part of this distribution.  The terms
+// are also available at
+// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt
+// Author: Iswarya
+// Organization: FOSSEE, IIT Bombay
+// Email: toolbox@scilab.in
+
 #include 
 #include "sci_iofunc.hpp"
 
diff --git a/sci_gateway/cpp/sci_sym_set_variables.cpp b/sci_gateway/cpp/sci_sym_set_variables.cpp
index 384de6f..9368d69 100644
--- a/sci_gateway/cpp/sci_sym_set_variables.cpp
+++ b/sci_gateway/cpp/sci_sym_set_variables.cpp
@@ -1,9 +1,14 @@
-/*
- * Implementation Symphony Tool Box for Scilab
- * set_variables.cpp
- * contains function for setting environment variables to their default and userdefined values in symphony
- * By Iswarya
- */
+// Copyright (C) 2015 - IIT Bombay - FOSSEE
+//
+// This file must be used under the terms of the CeCILL.
+// This source file is licensed as described in the file COPYING, which
+// you should have received as part of this distribution.  The terms
+// are also available at
+// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt
+// Author: Iswarya
+// Organization: FOSSEE, IIT Bombay
+// Email: toolbox@scilab.in
+
 #include 
 
 extern sym_environment* global_sym_env;//defined in globals.cpp
diff --git a/sci_gateway/cpp/sci_sym_setcolsoln.cpp b/sci_gateway/cpp/sci_sym_setcolsoln.cpp
index ca43779..aff6b4f 100644
--- a/sci_gateway/cpp/sci_sym_setcolsoln.cpp
+++ b/sci_gateway/cpp/sci_sym_setcolsoln.cpp
@@ -1,8 +1,14 @@
-/*
- * Symphony Toolbox
- * Function to set the solution to a known one
- * By Keyur Joshi
- */
+// Copyright (C) 2015 - IIT Bombay - FOSSEE
+//
+// This file must be used under the terms of the CeCILL.
+// This source file is licensed as described in the file COPYING, which
+// you should have received as part of this distribution.  The terms
+// are also available at
+// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt
+// Author: Keyur Joshi
+// Organization: FOSSEE, IIT Bombay
+// Email: toolbox@scilab.in
+
 #include "symphony.h"
 #include "sci_iofunc.hpp"
 
diff --git a/sci_gateway/cpp/sci_sym_setobj.cpp b/sci_gateway/cpp/sci_sym_setobj.cpp
index 3a4691a..571f752 100644
--- a/sci_gateway/cpp/sci_sym_setobj.cpp
+++ b/sci_gateway/cpp/sci_sym_setobj.cpp
@@ -1,8 +1,13 @@
-/*
- * Symphony Toolbox
- * Functions for setting the coefficients of the objective and the sense (minimization/maximization)
- * By Keyur Joshi
- */
+// Copyright (C) 2015 - IIT Bombay - FOSSEE
+//
+// This file must be used under the terms of the CeCILL.
+// This source file is licensed as described in the file COPYING, which
+// you should have received as part of this distribution.  The terms
+// are also available at
+// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt
+// Author: Keyur Joshi
+// Organization: FOSSEE, IIT Bombay
+// Email: toolbox@scilab.in
 
 #include "symphony.h"
 #include "sci_iofunc.hpp"
diff --git a/sci_gateway/cpp/sci_sym_solution.cpp b/sci_gateway/cpp/sci_sym_solution.cpp
index dff3b60..a918ac4 100644
--- a/sci_gateway/cpp/sci_sym_solution.cpp
+++ b/sci_gateway/cpp/sci_sym_solution.cpp
@@ -1,8 +1,14 @@
-/*
- * Symphony Toolbox
- * Provides the solution after the problem is solved
- * By Keyur Joshi
- */
+// Copyright (C) 2015 - IIT Bombay - FOSSEE
+//
+// This file must be used under the terms of the CeCILL.
+// This source file is licensed as described in the file COPYING, which
+// you should have received as part of this distribution.  The terms
+// are also available at
+// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt
+// Author: Keyur Joshi
+// Organization: FOSSEE, IIT Bombay
+// Email: toolbox@scilab.in
+
 #include "symphony.h"
 #include "sci_iofunc.hpp"
 
diff --git a/sci_gateway/cpp/sci_sym_solve.cpp b/sci_gateway/cpp/sci_sym_solve.cpp
index 4abb268..a0af480 100644
--- a/sci_gateway/cpp/sci_sym_solve.cpp
+++ b/sci_gateway/cpp/sci_sym_solve.cpp
@@ -1,8 +1,13 @@
-/*
- * Implementation Symphony Tool Box for Scilab
- * Contains sym_solve function
- * Author : Sai Kiran
- */
+// Copyright (C) 2015 - IIT Bombay - FOSSEE
+//
+// This file must be used under the terms of the CeCILL.
+// This source file is licensed as described in the file COPYING, which
+// you should have received as part of this distribution.  The terms
+// are also available at
+// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt
+// Author: Sai Kiran
+// Organization: FOSSEE, IIT Bombay
+// Email: toolbox@scilab.in
 
 #include 
 #include 
diff --git a/sci_gateway/cpp/sci_sym_varbounds.cpp b/sci_gateway/cpp/sci_sym_varbounds.cpp
index e0fa6ab..b650111 100644
--- a/sci_gateway/cpp/sci_sym_varbounds.cpp
+++ b/sci_gateway/cpp/sci_sym_varbounds.cpp
@@ -1,8 +1,14 @@
-/*
- * Symphony Toolbox
- * Functions for setting the upper and lower bounds of the variables
- * By Keyur Joshi
- */
+// Copyright (C) 2015 - IIT Bombay - FOSSEE
+//
+// This file must be used under the terms of the CeCILL.
+// This source file is licensed as described in the file COPYING, which
+// you should have received as part of this distribution.  The terms
+// are also available at
+// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt
+// Author: Keyur Joshi
+// Organization: FOSSEE, IIT Bombay
+// Email: toolbox@scilab.in
+
 #include "symphony.h"
 #include "sci_iofunc.hpp"
 
diff --git a/sci_gateway/cpp/sci_vartype.cpp b/sci_gateway/cpp/sci_vartype.cpp
index e6656a7..cc75735 100644
--- a/sci_gateway/cpp/sci_vartype.cpp
+++ b/sci_gateway/cpp/sci_vartype.cpp
@@ -1,8 +1,14 @@
-/*
- * Symphony Toolbox
- * Provides information about variables: is it continuous/integer/boolean?
- * By Keyur Joshi
- */
+// Copyright (C) 2015 - IIT Bombay - FOSSEE
+//
+// This file must be used under the terms of the CeCILL.
+// This source file is licensed as described in the file COPYING, which
+// you should have received as part of this distribution.  The terms
+// are also available at
+// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt
+// Author: Keyur Joshi
+// Organization: FOSSEE, IIT Bombay
+// Email: toolbox@scilab.in
+
 #include "symphony.h"
 #include "sci_iofunc.hpp"
 
diff --git a/sci_gateway/cpp/sym_data_query_functions.cpp b/sci_gateway/cpp/sym_data_query_functions.cpp
index 9f38094..ea9fe1b 100644
--- a/sci_gateway/cpp/sym_data_query_functions.cpp
+++ b/sci_gateway/cpp/sym_data_query_functions.cpp
@@ -1,9 +1,13 @@
-/*
- * Implementation Symphony Tool Box for Scilab
- * sym_data_query_functions.cpp
- * contains Data Query Functions( 13 functions)
- * Author: Sai Kiran
- */
+// Copyright (C) 2015 - IIT Bombay - FOSSEE
+//
+// This file must be used under the terms of the CeCILL.
+// This source file is licensed as described in the file COPYING, which
+// you should have received as part of this distribution.  The terms
+// are also available at
+// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt
+// Author: Sai Kiran
+// Organization: FOSSEE, IIT Bombay
+// Email: toolbox@scilab.in
 
 #include 
 #include 
diff --git a/sci_gateway/cpp/template.cpp b/sci_gateway/cpp/template.cpp
deleted file mode 100644
index a835fc3..0000000
--- a/sci_gateway/cpp/template.cpp
+++ /dev/null
@@ -1,43 +0,0 @@
-/*
- * Symphony Toolbox
- * 
- * 
- */
-#include "symphony.h"
-#include "sci_iofunc.hpp"
-
-extern sym_environment* global_sym_env; //defined in globals.cpp
-
-extern "C" {
-#include "api_scilab.h"
-#include "Scierror.h"
-#include "sciprint.h"
-#include "BOOL.h"
-#include 
-
-int sci_template(char *fname){
-	
-	//error management variable
-	SciErr sciErr;
-	int iRet;
-	
-	//data declarations
-	
-	//ensure that environment is active
-	if(global_sym_env==NULL){
-		sciprint("Error: Symphony environment not initialized. Please run 'sym_open()' first.\n");
-		return 1;
-	}
-	
-	//code to check arguments and get them
-	CheckInputArgument(pvApiCtx, , ) ;
-	CheckOutputArgument(pvApiCtx, , ) ;
-	
-	//code to process input
-	
-	//code to give output
-	
-	return 0;
-}
-
-}
diff --git a/tests/general_tests/lsqlin/lsqlin_A1.sce b/tests/general_tests/lsqlin/lsqlin_A1.sce
index ba9b5be..1c2128b 100644
--- a/tests/general_tests/lsqlin/lsqlin_A1.sce
+++ b/tests/general_tests/lsqlin/lsqlin_A1.sce
@@ -1,23 +1,17 @@
 // Check for elements in A
-C = [0.9501    0.7620    0.6153    0.4057
-	 0.2311    0.4564    0.7919    0.9354
-	 0.6068    0.0185    0.9218    0.9169
-	 0.4859    0.8214    0.7382    0.4102
-	 0.8912    0.4447    0.1762    0.8936];
-d = [0.0578
-	 0.3528
-	 0.8131
-	 0.0098
-	 0.1388];
-A = [0.2027    0.2721    0.7467    0.4659 0
-	 0.1987    0.1988    0.4450    0.4186 0
-	 0.6037    0.0152    0.9318    0.8462 0];
-b = [0.5251
-	 0.2026
-	 0.6721];
+C = [2 0;
+	-1 1;
+	 0 2]
+d = [1
+	 0
+    -1];
+A = [10 -2 0;
+	 -2 10 0];
+b = [4
+    -4];
 
 //Error
-//lsqlin: The number of columns in A must be the same as the number of elements of d
+//lsqlin: The number of columns in A must be the same as the number of columns in C
 //at line     213 of function lsqlin called by :  
 //[xopt,resnorm,residual,exitflag,output,lambda] = lsqlin(C,d,A,b)
 
diff --git a/tests/general_tests/lsqlin/lsqlin_Aeq1.sce b/tests/general_tests/lsqlin/lsqlin_Aeq1.sce
index 458cbb0..f574e9d 100644
--- a/tests/general_tests/lsqlin/lsqlin_Aeq1.sce
+++ b/tests/general_tests/lsqlin/lsqlin_Aeq1.sce
@@ -1,23 +1,18 @@
 // Check for elements in Aeq
-C = [0.9501    0.7620    0.6153    0.4057
-	 0.2311    0.4564    0.7919    0.9354
-	 0.6068    0.0185    0.9218    0.9169
-	 0.4859    0.8214    0.7382    0.4102
-	 0.8912    0.4447    0.1762    0.8936];
-d = [0.0578
-	 0.3528
-	 0.8131
-	 0.0098
-	 0.1388];
-Aeq = [0.2027    0.2721    0.7467    0.4659 0
-	 0.1987    0.1988    0.4450    0.4186 0
-	 0.6037    0.0152    0.9318    0.8462 0];
-beq = [0.5251
-	 0.2026
-	 0.6721];
+ C = [2 0;
+	 -1 1;
+	  0 2]
+ d = [1
+ 	 0
+    -1];
+ Aeq = [10 -2 0;
+	  -2 10 0];
+ beq = [4
+     -4];
+
 
 //Error
-//lsqlin: The number of columns in Aeq must be the same as the number of elements of d
+//lsqlin: The number of columns in Aeq must be the same as the number of columns in C
 //at line     219 of function lsqlin called by :  
 //[xopt,resnorm,residual,exitflag,output,lambda] = lsqlin(C,d,[],[],Aeq,beq)
 
diff --git a/tests/general_tests/lsqlin/lsqlin_C1.sce b/tests/general_tests/lsqlin/lsqlin_C1.sce
index 55fe976..6da1d49 100644
--- a/tests/general_tests/lsqlin/lsqlin_C1.sce
+++ b/tests/general_tests/lsqlin/lsqlin_C1.sce
@@ -1,21 +1,13 @@
 // Check for elements in C and d
-C = [0.9501    0.7620    0.6153    0.4057
-	 0.2311    0.4564    0.7919    0.9354
-	 0.6068    0.0185    0.9218    0.9169
-	 0.4859    0.8214    0.7382    0.4102
-	 0.8912    0.4447    0.1762    0.8936];
-d = [0.0578
-	 0.3528
-	 0.8131
-	 0.0098
-	 0.1388
-	 0.2536];
-A = [0.2027    0.2721    0.7467    0.4659
-	 0.1987    0.1988    0.4450    0.4186
-	 0.6037    0.0152    0.9318    0.8462];
-b = [0.5251
-	 0.2026
-	 0.6721];
+C = [2 0;
+	-1 1;
+	 0 2]
+d = [1
+	 0];
+A = [10 -2;
+	 -2 10];
+b = [4
+    -4];
 
 //Error
 //lsqlin: The number of rows in C must be equal the number of elements of d
diff --git a/tests/general_tests/lsqlin/lsqlin_b1.sce b/tests/general_tests/lsqlin/lsqlin_b1.sce
index 49b6dec..fb01b79 100644
--- a/tests/general_tests/lsqlin/lsqlin_b1.sce
+++ b/tests/general_tests/lsqlin/lsqlin_b1.sce
@@ -1,18 +1,13 @@
 // Check for elements in b
-C = [0.9501    0.7620    0.6153    0.4057
-	 0.2311    0.4564    0.7919    0.9354
-	 0.6068    0.0185    0.9218    0.9169
-	 0.4859    0.8214    0.7382    0.4102
-	 0.8912    0.4447    0.1762    0.8936];
-d = [0.0578
-	 0.3528
-	 0.8131
-	 0.0098
-	 0.1388];
-A = [0.2027    0.2721    0.7467    0.4659
-	 0.1987    0.1988    0.4450    0.4186
-	 0.6037    0.0152    0.9318    0.8462];
-b = [-%inf -%inf -%inf];
+C = [2 0;
+	 -1 1;
+	  0 2]
+d = [1
+ 	 0
+    -1];
+A = [10 -2;
+	  -2 10];
+b = [-%inf -%inf];
 
 //Error
 //lsqlin: Value of b can not be negative infinity
diff --git a/tests/general_tests/lsqlin/lsqlin_beq1.sce b/tests/general_tests/lsqlin/lsqlin_beq1.sce
index b71486e..38056c0 100644
--- a/tests/general_tests/lsqlin/lsqlin_beq1.sce
+++ b/tests/general_tests/lsqlin/lsqlin_beq1.sce
@@ -1,18 +1,13 @@
 // Check for elements in beq
-C = [0.9501    0.7620    0.6153    0.4057
-	 0.2311    0.4564    0.7919    0.9354
-	 0.6068    0.0185    0.9218    0.9169
-	 0.4859    0.8214    0.7382    0.4102
-	 0.8912    0.4447    0.1762    0.8936];
-d = [0.0578
-	 0.3528
-	 0.8131
-	 0.0098
-	 0.1388];
-Aeq = [0.2027    0.2721    0.7467    0.4659
-	 0.1987    0.1988    0.4450    0.4186
-	 0.6037    0.0152    0.9318    0.8462];
-beq = [-%inf -%inf -%inf];
+C = [2 0;
+	-1 1;
+	 0 2]
+d = [1
+ 	 0
+    -1];
+Aeq = [10 -2;
+	  -2 10];
+beq = [-%inf -%inf];
 
 //Error
 //lsqlin: Value of beq can not be negative infinity
diff --git a/tests/general_tests/lsqlin/lsqlin_infeasible1.sce b/tests/general_tests/lsqlin/lsqlin_infeasible1.sce
index a66dd0f..9509309 100644
--- a/tests/general_tests/lsqlin/lsqlin_infeasible1.sce
+++ b/tests/general_tests/lsqlin/lsqlin_infeasible1.sce
@@ -1,15 +1,11 @@
 // Check for the infeasible problem
-C = [0.9501    0.7620    0.6153    0.4057
-	 0.2311    0.4564    0.7919    0.9354
-	 0.6068    0.0185    0.9218    0.9169
-	 0.4859    0.8214    0.7382    0.4102
-	 0.8912    0.4447    0.1762    0.8936];
-d = [0.0578
-	 0.3528
-	 0.8131
-	 0.0098
-	 0.1388];
-Aeq = [-1 0 0 0; 0 -1 0 0; 1 1 0 0 ];
+C = [2 0 0;
+	-1 1 0;
+	 0 2 0]
+d = [1
+	 0
+    -1];
+Aeq = [-1 0 0; 0 -1 0; 1 1 0 ];
 beq = [-6 -6 11];
 
 //Converged to a point of local infeasibility.
@@ -27,17 +23,14 @@ beq = [-6 -6 11];
 //  5  
 // residual  =
 // 
-//    0.0578  
-//    0.3528  
-//    0.8131  
-//    0.0098  
-//    0.1388  
+//    1.  
+//    0.  
+//  - 1.  
 // resnorm  =
 // 
-//    0.8083018  
+//    2.  
 // xopt  =
 // 
 //     []
-
 [xopt,resnorm,residual,exitflag,output,lambda] = lsqlin(C,d,[],[],Aeq,beq)
 
diff --git a/tests/general_tests/lsqlin/lsqlin_input1.sce b/tests/general_tests/lsqlin/lsqlin_input1.sce
index 7521f61..5b97670 100644
--- a/tests/general_tests/lsqlin/lsqlin_input1.sce
+++ b/tests/general_tests/lsqlin/lsqlin_input1.sce
@@ -1,21 +1,14 @@
 // Check for the input arguments
-C = [0.9501    0.7620    0.6153    0.4057
-	 0.2311    0.4564    0.7919    0.9354
-	 0.6068    0.0185    0.9218    0.9169
-	 0.4859    0.8214    0.7382    0.4102
-	 0.8912    0.4447    0.1762    0.8936];
-d = [0.0578
-	 0.3528
-	 0.8131
-	 0.0098
-	 0.1388];
-A = [0.2027    0.2721    0.7467    0.4659
-	 0.1987    0.1988    0.4450    0.4186
-	 0.6037    0.0152    0.9318    0.8462];
-b = [0.5251
-	 0.2026
-	 0.6721];
-
+C = [2 0;
+	-1 1;
+	 0 2]
+d = [1
+	 0
+    -1];
+A = [10 -2;
+	 -2 10];
+b = [4
+    -4];
 //Error
 //lsqlin: Unexpected number of input arguments : 2 provided while should be in the set of [4 6 8 9 10]
 //at line      99 of function lsqlin called by :  
diff --git a/tests/general_tests/lsqlin/lsqlin_input2.sce b/tests/general_tests/lsqlin/lsqlin_input2.sce
index 9826583..dbfe187 100644
--- a/tests/general_tests/lsqlin/lsqlin_input2.sce
+++ b/tests/general_tests/lsqlin/lsqlin_input2.sce
@@ -1,20 +1,14 @@
 // Check for the input arguments
-C = [0.9501    0.7620    0.6153    0.4057
-	 0.2311    0.4564    0.7919    0.9354
-	 0.6068    0.0185    0.9218    0.9169
-	 0.4859    0.8214    0.7382    0.4102
-	 0.8912    0.4447    0.1762    0.8936];
-d = [0.0578
-	 0.3528
-	 0.8131
-	 0.0098
-	 0.1388];
-A = [0.2027    0.2721    0.7467    0.4659
-	 0.1987    0.1988    0.4450    0.4186
-	 0.6037    0.0152    0.9318    0.8462];
-b = [0.5251
-	 0.2026
-	 0.6721];
+C = [2 0;
+	-1 1;
+	 0 2]
+d = [1
+	 0
+    -1];
+A = [10 -2;
+	 -2 10];
+b = [4
+    -4];
 
 //Error
 //lsqlin: Unexpected number of input arguments : 14 provided while should be in the set of [4 6 8 9 10]
diff --git a/tests/general_tests/lsqlin/lsqlin_logical1.sce b/tests/general_tests/lsqlin/lsqlin_logical1.sce
index bc0dc06..959cc7c 100644
--- a/tests/general_tests/lsqlin/lsqlin_logical1.sce
+++ b/tests/general_tests/lsqlin/lsqlin_logical1.sce
@@ -1,51 +1,41 @@
-// Check for elements in A
-C = [0.9501    0.7620    0.6153    0.4057
-	 0.2311    0.4564    0.7919    0.9354
-	 0.6068    0.0185    0.9218    0.9169
-	 0.4859    0.8214    0.7382    0.4102
-	 0.8912    0.4447    0.1762    0.8936];
-d = [0.0578
-	 0.3528
-	 0.8131
-	 0.0098
-	 0.1388];
-A = [0.2027    0.2721    0.7467    0.4659
-	 0.1987    0.1988    0.4450    0.4186
-	 0.6037    0.0152    0.9318    0.8462];
-b = [0.5251
-	 0.2026
-	 0.6721];
+// An example with inequality constraints
+C = [2 0;
+	-1 1;
+	 0 2]
+d = [1
+	 0
+    -1];
+A = [10 -2;
+	 -2 10];
+b = [4
+    -4];
 
-// Output
+//Output
 //Optimal Solution Found.
 // lambda  =
 // 
-//   lower: [0,0,0,0]
-//   upper: [0,0,0,0]
+//   lower: [0,0]
+//   upper: [0,0]
 //   eqlin: [0x0 constant]
-//   ineqlin: [8.697D-11,0.0925859,0.1118582]
+//   ineqlin: [0.0000422,0.0000089]
 // output  =
 // 
-//   Iterations: 7
+//   Iterations: 13
 // exitflag  =
 // 
 //  0  
 // residual  =
 // 
-//    0.0126031  
-//    0.0208054  
-//    0.1295085  
-//    0.0057397  
-//  - 0.0137246  
+//    0.3335021  
+//    0.6666002  
+//  - 0.3332976  
 // resnorm  =
 // 
-//    0.0175855  
+//    0.6666667  
 // xopt  =
 // 
-//    0.1298639  
-//  - 0.5756958  
-//    0.4251033  
-//    0.2438436  
+//    0.3332490  
+//  - 0.3333512  
 
 [xopt,resnorm,residual,exitflag,output,lambda] = lsqlin(C,d,A,b)
 
diff --git a/tests/general_tests/lsqlin/lsqlin_maxiter.sce b/tests/general_tests/lsqlin/lsqlin_maxiter.sce
new file mode 100644
index 0000000..036cf39
--- /dev/null
+++ b/tests/general_tests/lsqlin/lsqlin_maxiter.sce
@@ -0,0 +1,42 @@
+// Check for maxiter
+C = [2 0;
+	-1 1;
+	 0 2]
+d = [1
+	 0
+    -1];
+A = [10 -2;
+	 -2 10];
+b = [4
+    -4];
+options = list("MaxIter",1);
+// Output
+//Maximum Number of Iterations Exceeded. Output may not be optimal.
+// lambda  =
+// 
+//   lower: [0x0 constant]
+//   upper: [0x0 constant]
+//   eqlin: [0x0 constant]
+//   ineqlin: [0x0 constant]
+// output  =
+// 
+//   Iterations: 1
+// exitflag  =
+// 
+//  1  
+// residual  =
+// 
+//    1.0243179  
+//    0.3941271  
+//  - 0.1874278  
+// resnorm  =
+// 
+//    1.2396926  
+// xopt  =
+// 
+//  - 0.0121590  
+//  - 0.4062861 
+
+[xopt,resnorm,residual,exitflag,output,lambda] = lsqlin(C,d,A,b,[],[],[],[],[],options)
+
+
diff --git a/tests/general_tests/lsqlin/lsqlin_param1.sce b/tests/general_tests/lsqlin/lsqlin_param1.sce
index a9d24bc..65aff86 100644
--- a/tests/general_tests/lsqlin/lsqlin_param1.sce
+++ b/tests/general_tests/lsqlin/lsqlin_param1.sce
@@ -1,20 +1,14 @@
 // Check for the param to be a list
-C = [0.9501    0.7620    0.6153    0.4057
-	 0.2311    0.4564    0.7919    0.9354
-	 0.6068    0.0185    0.9218    0.9169
-	 0.4859    0.8214    0.7382    0.4102
-	 0.8912    0.4447    0.1762    0.8936];
-d = [0.0578
-	 0.3528
-	 0.8131
-	 0.0098
-	 0.1388];
-A = [0.2027    0.2721    0.7467    0.4659
-	 0.1987    0.1988    0.4450    0.4186
-	 0.6037    0.0152    0.9318    0.8462];
-b = [0.5251
-	 0.2026
-	 0.6721];
+C = [2 0;
+	-1 1;
+	 0 2]
+d = [1
+	 0
+    -1];
+A = [10 -2;
+	 -2 10];
+b = [4
+    -4];
 param = 0;
 
 //Error
diff --git a/tests/general_tests/lsqlin/lsqlin_param2.sce b/tests/general_tests/lsqlin/lsqlin_param2.sce
index bb7cffa..f780759 100644
--- a/tests/general_tests/lsqlin/lsqlin_param2.sce
+++ b/tests/general_tests/lsqlin/lsqlin_param2.sce
@@ -1,20 +1,14 @@
 // Check for the param to be even in number
-C = [0.9501    0.7620    0.6153    0.4057
-	 0.2311    0.4564    0.7919    0.9354
-	 0.6068    0.0185    0.9218    0.9169
-	 0.4859    0.8214    0.7382    0.4102
-	 0.8912    0.4447    0.1762    0.8936];
-d = [0.0578
-	 0.3528
-	 0.8131
-	 0.0098
-	 0.1388];
-A = [0.2027    0.2721    0.7467    0.4659
-	 0.1987    0.1988    0.4450    0.4186
-	 0.6037    0.0152    0.9318    0.8462];
-b = [0.5251
-	 0.2026
-	 0.6721];
+C = [2 0;
+	-1 1;
+	 0 2]
+d = [1
+	 0
+    -1];
+A = [10 -2;
+	 -2 10];
+b = [4
+    -4];
 param = list("MaxIter");
 
 //Error
diff --git a/tests/general_tests/lsqlin/lsqlin_param3.sce b/tests/general_tests/lsqlin/lsqlin_param3.sce
index 60e960d..283c6e2 100644
--- a/tests/general_tests/lsqlin/lsqlin_param3.sce
+++ b/tests/general_tests/lsqlin/lsqlin_param3.sce
@@ -1,20 +1,14 @@
 // Check for the param to be even in number
-C = [0.9501    0.7620    0.6153    0.4057
-	 0.2311    0.4564    0.7919    0.9354
-	 0.6068    0.0185    0.9218    0.9169
-	 0.4859    0.8214    0.7382    0.4102
-	 0.8912    0.4447    0.1762    0.8936];
-d = [0.0578
-	 0.3528
-	 0.8131
-	 0.0098
-	 0.1388];
-A = [0.2027    0.2721    0.7467    0.4659
-	 0.1987    0.1988    0.4450    0.4186
-	 0.6037    0.0152    0.9318    0.8462];
-b = [0.5251
-	 0.2026
-	 0.6721];
+C = [2 0;
+	-1 1;
+	 0 2]
+d = [1
+	 0
+    -1];
+A = [10 -2;
+	 -2 10];
+b = [4
+    -4];
 param = list("Iter",300);
 
 //Error
diff --git a/tests/general_tests/lsqlin/lsqlin_x01.sce b/tests/general_tests/lsqlin/lsqlin_x01.sce
index 3b0f6f9..d014443 100644
--- a/tests/general_tests/lsqlin/lsqlin_x01.sce
+++ b/tests/general_tests/lsqlin/lsqlin_x01.sce
@@ -1,20 +1,14 @@
-// Check for elements in A
-C = [0.9501    0.7620    0.6153    0.4057
-	 0.2311    0.4564    0.7919    0.9354
-	 0.6068    0.0185    0.9218    0.9169
-	 0.4859    0.8214    0.7382    0.4102
-	 0.8912    0.4447    0.1762    0.8936];
-d = [0.0578
-	 0.3528
-	 0.8131
-	 0.0098
-	 0.1388];
-A = [0.2027    0.2721    0.7467    0.4659
-	 0.1987    0.1988    0.4450    0.4186
-	 0.6037    0.0152    0.9318    0.8462];
-b = [0.5251
-	 0.2026
-	 0.6721];
+// Test for intial guess
+C = [2 0;
+	-1 1;
+	 0 2]
+d = [1
+	 0
+    -1];
+A = [10 -2;
+	 -2 10];
+b = [4
+    -4];
 x0 = [0 0 0];
 
 //WARNING: lsqlin: Ignoring initial guess of variables as it is not equal to the number of variables
@@ -22,32 +16,28 @@ x0 = [0 0 0];
 //Optimal Solution Found.
 // lambda  =
 // 
-//   lower: [0,0,0,0]
-//   upper: [0,0,0,0]
+//   lower: [0,0]
+//   upper: [0,0]
 //   eqlin: [0x0 constant]
-//   ineqlin: [8.697D-11,0.0925859,0.1118582]
+//   ineqlin: [0.0000422,0.0000089]
 // output  =
 // 
-//   Iterations: 7
+//   Iterations: 13
 // exitflag  =
 // 
 //  0  
 // residual  =
 // 
-//    0.0126031  
-//    0.0208054  
-//    0.1295085  
-//    0.0057397  
-//  - 0.0137246  
+//    0.3335021  
+//    0.6666002  
+//  - 0.3332976  
 // resnorm  =
 // 
-//    0.0175855  
+//    0.6666667  
 // xopt  =
 // 
-//    0.1298639  
-//  - 0.5756958  
-//    0.4251033  
-//    0.2438436 
+//    0.3332490  
+//  - 0.3333512  
 
 [xopt,resnorm,residual,exitflag,output,lambda] = lsqlin(C,d,A,b,[],[],[],[],x0)
 
diff --git a/tests/general_tests/lsqlin/lsqlin_zeros.sce b/tests/general_tests/lsqlin/lsqlin_zeros.sce
new file mode 100644
index 0000000..2695ff1
--- /dev/null
+++ b/tests/general_tests/lsqlin/lsqlin_zeros.sce
@@ -0,0 +1,32 @@
+// Check for all zeros
+C = [0];
+d = [0];
+A = [0];
+b = [0];
+// Output
+//Optimal Solution Found.
+// lambda  =
+// 
+//   lower: 0
+//   upper: 0
+//   eqlin: [0x0 constant]
+//   ineqlin: 0.999901
+// output  =
+// 
+//   Iterations: 2
+// exitflag  =
+// 
+//  0  
+// residual  =
+// 
+//    0.  
+// resnorm  =
+// 
+//    0.  
+// xopt  =
+// 
+//    0.     
+
+[xopt,resnorm,residual,exitflag,output,lambda] = lsqlin(C,d,A,b)
+
+
diff --git a/tests/general_tests/lsqnonneg/lsqnonneg_C.sce b/tests/general_tests/lsqnonneg/lsqnonneg_C.sce
index f792559..90f5b43 100644
--- a/tests/general_tests/lsqnonneg/lsqnonneg_C.sce
+++ b/tests/general_tests/lsqnonneg/lsqnonneg_C.sce
@@ -1,15 +1,13 @@
 // Check for the size of C and d
-C = [
-	0.0372    0.2869
-	0.6861    0.7071
-	0.6233    0.6245
-	0.6344    0.6170];
-d = [
-	0.8587
-	0.1781
-	0.0747
-    0.8405
-	0.2356];
+C = [1 1 1;
+	1 1 0;
+	0 1 1;
+	1 0 0;
+	0 0 1]
+d = [89;
+	67;
+	53;
+	35]
 
 // Error
 //lsqlin: The number of rows in C must be equal the number of elements of d
diff --git a/tests/general_tests/lsqnonneg/lsqnonneg_input1.sce b/tests/general_tests/lsqnonneg/lsqnonneg_input1.sce
index eb0aafc..db1e461 100644
--- a/tests/general_tests/lsqnonneg/lsqnonneg_input1.sce
+++ b/tests/general_tests/lsqnonneg/lsqnonneg_input1.sce
@@ -1,14 +1,14 @@
 // Check for the input arguments
-C = [
-	0.0372    0.2869
-	0.6861    0.7071
-	0.6233    0.6245
-	0.6344    0.6170];
-d = [
-	0.8587
-	0.1781
-	0.0747
-    0.8405];
+C = [1 1 1;
+	1 1 0;
+	0 1 1;
+	1 0 0;
+	0 0 1]
+d = [89;
+	67;
+	53;
+	35;
+	20]
 
 // Error
 //lsqlin: Unexpected number of input arguments : 1 provided while should be in the set of [2 3]
diff --git a/tests/general_tests/lsqnonneg/lsqnonneg_input2.sce b/tests/general_tests/lsqnonneg/lsqnonneg_input2.sce
index 2d2e557..9da3f66 100644
--- a/tests/general_tests/lsqnonneg/lsqnonneg_input2.sce
+++ b/tests/general_tests/lsqnonneg/lsqnonneg_input2.sce
@@ -1,14 +1,14 @@
 // Check for the input arguments
-C = [
-	0.0372    0.2869
-	0.6861    0.7071
-	0.6233    0.6245
-	0.6344    0.6170];
-d = [
-	0.8587
-	0.1781
-	0.0747
-    0.8405];
+C = [1 1 1;
+	1 1 0;
+	0 1 1;
+	1 0 0;
+	0 0 1]
+d = [89;
+	67;
+	53;
+	35;
+	20]
 
 param = list();
 x0 = [0 0];
diff --git a/tests/general_tests/lsqnonneg/lsqnonneg_logical.sce b/tests/general_tests/lsqnonneg/lsqnonneg_logical.sce
index 15106f0..5f59f8c 100644
--- a/tests/general_tests/lsqnonneg/lsqnonneg_logical.sce
+++ b/tests/general_tests/lsqnonneg/lsqnonneg_logical.sce
@@ -1,40 +1,42 @@
 // An example with C and d as input
-C = [
-	0.0372    0.2869
-	0.6861    0.7071
-	0.6233    0.6245
-	0.6344    0.6170];
-d = [
-	0.8587
-	0.1781
-	0.0747
-    0.8405];
+C = [1 1 1;
+	1 1 0;
+	0 1 1;
+	1 0 0;
+	0 0 1]
+d = [89;
+	67;
+	53;
+	35;
+	20]
 
 //Output
 //Optimal Solution Found.
 // lambda  =
 // 
-//   lower: [0.1506118,1.441D-11]
-//   upper: [0,0]
+//   lower: [5.131D-09,5.546D-09,8.739D-09]
+//   upper: [0,0,0]
 // output  =
 // 
-//   Iterations: 5
+//   Iterations: 7
 // exitflag  =
 // 
 //  0  
 // residual  =
 // 
-//    0.6598971  
-//  - 0.3118739  
-//  - 0.3580375  
-//    0.4129595  
+//    0.75   
+//  - 0.625  
+//  - 0.125  
+//  - 0.125  
+//  - 0.625  
 // resnorm  =
 // 
-//    0.8314560  
+//    1.375  
 // xopt  =
 // 
-//    0.         
-//    0.6929344  
+//    35.125  
+//    32.5    
+//    20.625  
 
 [xopt,resnorm,residual,exitflag,output,lambda] = lsqnonneg(C,d)
 
diff --git a/tests/general_tests/lsqnonneg/lsqnonneg_maxiter.sce b/tests/general_tests/lsqnonneg/lsqnonneg_maxiter.sce
new file mode 100644
index 0000000..fa61c73
--- /dev/null
+++ b/tests/general_tests/lsqnonneg/lsqnonneg_maxiter.sce
@@ -0,0 +1,42 @@
+// Check for maxiter
+C = [1 1 1;
+	1 1 0;
+	0 1 1;
+	1 0 0;
+	0 0 1]
+d = [89;
+	67;
+	53;
+	35;
+	20]
+options = list("MaxIter",1)
+//Output
+//Maximum Number of Iterations Exceeded. Output may not be optimal.
+// lambda  =
+// 
+//   lower: [0x0 constant]
+//   upper: [0x0 constant]
+// output  =
+// 
+//   Iterations: 1
+// exitflag  =
+// 
+//  1  
+// residual  =
+// 
+//    83.449663  
+//    63.182327  
+//    49.319768  
+//    33.129895  
+//    18.267336  
+// resnorm  =
+// 
+//    14819.578  
+// xopt  =
+// 
+//    1.8701046  
+//    1.9475681  
+//    1.7326638  
+
+[xopt,resnorm,residual,exitflag,output,lambda] = lsqnonneg(C,d,options)
+
diff --git a/tests/general_tests/lsqnonneg/lsqnonneg_param1.sce b/tests/general_tests/lsqnonneg/lsqnonneg_param1.sce
index d91f4b1..9809964 100644
--- a/tests/general_tests/lsqnonneg/lsqnonneg_param1.sce
+++ b/tests/general_tests/lsqnonneg/lsqnonneg_param1.sce
@@ -1,14 +1,14 @@
 // Check for the parameters to be a list
-C = [
-	0.0372    0.2869
-	0.6861    0.7071
-	0.6233    0.6245
-	0.6344    0.6170];
-d = [
-	0.8587
-	0.1781
-	0.0747
-    0.8405];
+C = [1 1 1;
+	1 1 0;
+	0 1 1;
+	1 0 0;
+	0 0 1]
+d = [89;
+	67;
+	53;
+	35;
+	20]
 
 param = 0;
 
diff --git a/tests/general_tests/lsqnonneg/lsqnonneg_param2.sce b/tests/general_tests/lsqnonneg/lsqnonneg_param2.sce
index 557a836..9cbaa72 100644
--- a/tests/general_tests/lsqnonneg/lsqnonneg_param2.sce
+++ b/tests/general_tests/lsqnonneg/lsqnonneg_param2.sce
@@ -1,14 +1,14 @@
 // Check for the size of parameters
-C = [
-	0.0372    0.2869
-	0.6861    0.7071
-	0.6233    0.6245
-	0.6344    0.6170];
-d = [
-	0.8587
-	0.1781
-	0.0747
-    0.8405];
+C = [1 1 1;
+	1 1 0;
+	0 1 1;
+	1 0 0;
+	0 0 1]
+d = [89;
+	67;
+	53;
+	35;
+	20]
 
 param = list("MaxIter");
 
diff --git a/tests/general_tests/lsqnonneg/lsqnonneg_zeros.sce b/tests/general_tests/lsqnonneg/lsqnonneg_zeros.sce
new file mode 100644
index 0000000..e454f42
--- /dev/null
+++ b/tests/general_tests/lsqnonneg/lsqnonneg_zeros.sce
@@ -0,0 +1,28 @@
+// Check for all zeros as input
+C = [0];
+d = [0];
+
+//Output
+//Optimal Solution Found.
+// lambda  =
+// 
+//   lower: 6.400D-11
+//   upper: 0
+// output  =
+// 
+//   Iterations: 3
+// exitflag  =
+// 
+//  0  
+// residual  =
+// 
+//    0.  
+// resnorm  =
+// 
+//    0.  
+// xopt  =
+// 
+//    0.9124275  
+
+[xopt,resnorm,residual,exitflag,output,lambda] = lsqnonneg(C,d)
+
diff --git a/tests/general_tests/qpipopt/qpipopt_logical_2.sce b/tests/general_tests/qpipopt/qpipopt_logical_2.sce
index a61e416..7a75ebe 100644
--- a/tests/general_tests/qpipopt/qpipopt_logical_2.sce
+++ b/tests/general_tests/qpipopt/qpipopt_logical_2.sce
@@ -1,4 +1,3 @@
-
 // A simple example without constraints
 A= [];
 conLB=[];
diff --git a/tests/general_tests/qpipopt/qpipopt_maxiter.sce b/tests/general_tests/qpipopt/qpipopt_maxiter.sce
new file mode 100644
index 0000000..5851630
--- /dev/null
+++ b/tests/general_tests/qpipopt/qpipopt_maxiter.sce
@@ -0,0 +1,46 @@
+//Find x in R^6 such that:
+// A simple example with constraints
+A= [1,-1,1,0,3,1;
+-1,0,-3,-4,5,6;
+2,5,3,0,1,0
+0,1,0,1,2,-1;
+-1,0,2,1,1,0];
+conLB=[1;2;3;-%inf;-%inf];
+conUB = [1;2;3;-1;2.5];
+lb=[-1000;-10000; 0; -1000; -1000; -1000];
+ub=[10000; 100; 1.5; 100; 100; 1000];
+//and minimize 0.5*x'*H*x + f'*x with
+f=[1; 2; 3; 4; 5; 6]; H=eye(6,6);
+nbVar = 6;
+nbCon = 5;
+x0 = repmat(0,nbVar,1);
+param = list("MaxIter", 1);
+
+//Output
+//Maximum Number of Iterations Exceeded. Output may not be optimal.
+// lambda  =
+// 
+//   lower: [0x0 constant]
+//   upper: [0x0 constant]
+//   constraint: [0x0 constant]
+// output  =
+// 
+//   Iterations: 1
+// exitflag  =
+// 
+//  1  
+// fopt  =
+// 
+//  - 9.9784864  
+// xopt  =
+// 
+//    0.9103357  
+//  - 0.1104630  
+//    0.0476471  
+//  - 2.0070896  
+//    0.2116560  
+//  - 1.1624291 
+
+[xopt,fopt,exitflag,output,lambda]=qpipopt(nbVar,nbCon,H,f,lb,ub,A,conLB,conUB,x0,param)
+
+
diff --git a/tests/general_tests/qpipopt/qpipopt_zeros.sce b/tests/general_tests/qpipopt/qpipopt_zeros.sce
new file mode 100644
index 0000000..9604c4d
--- /dev/null
+++ b/tests/general_tests/qpipopt/qpipopt_zeros.sce
@@ -0,0 +1,42 @@
+
+// A simple example without constraints and all values to zero
+A= [];
+conLB=[];
+conUB = [];
+lb=[];
+ub=[];
+f=[0]'; 
+H =[0];
+nbVar = 1;
+nbCon = 0;
+
+
+//Output
+//
+//Optimal Solution Found.
+
+//   lower: 0
+//   upper: 0
+//   constraint: [0x0 constant]
+// 
+// lambda   
+// 
+//   Iterations: 0
+// 
+// output   
+// 
+//  0  
+// 
+// exitflag   
+// 
+//    0.  
+// 
+// fopt   
+// 
+//    0.  
+// 
+// xopt 
+
+[xopt,fopt,exitflag,output,lambda]=qpipopt(nbVar,nbCon,H,f,lb,ub,A,conLB,conUB)
+
+
diff --git a/tests/general_tests/qpipoptmat/qpipoptmat_logical2.sce b/tests/general_tests/qpipoptmat/qpipoptmat_logical2.sce
index 9f11d1d..e316253 100644
--- a/tests/general_tests/qpipoptmat/qpipoptmat_logical2.sce
+++ b/tests/general_tests/qpipoptmat/qpipoptmat_logical2.sce
@@ -11,7 +11,7 @@ b = [-1; 2.5];
 lb=[-1000; -10000; 0; -1000; -1000; -1000];
 ub=[10000; 100; 1.5; 100; 100; 1000];
 param = list("MaxIter", 300, "CpuTime",100);
-//and minimize 0.5*x'*Q*x + p'*x with
+//and minimize 0.5*x'*H*x + f'*x with
 f=[1; 2; 3; 4; 5; 6]; H=eye(6,6);
 [xopt,fopt,exitflag,output,lambda]=qpipoptmat(H,f,A,b,Aeq,beq,lb,ub,[],param);
 
diff --git a/tests/general_tests/qpipoptmat/qpipoptmat_maxiter.sce b/tests/general_tests/qpipoptmat/qpipoptmat_maxiter.sce
new file mode 100644
index 0000000..5e4b0e5
--- /dev/null
+++ b/tests/general_tests/qpipoptmat/qpipoptmat_maxiter.sce
@@ -0,0 +1,44 @@
+//Find x in R^6 such that:
+// A simple example with constraints
+
+Aeq= [1,-1,1,0,3,1;
+-1,0,-3,-4,5,6;
+2,5,3,0,1,0];
+beq=[1; 2; 3];
+A= [0,1,0,1,2,-1;
+-1,0,2,1,1,0];
+b = [-1; 2.5];
+lb=[-1000; -10000; 0; -1000; -1000; -1000];
+ub=[10000; 100; 1.5; 100; 100; 1000];
+param = list("MaxIter", 1);
+//and minimize 0.5*x'*H*x + f'*x with
+f=[1; 2; 3; 4; 5; 6]; H=eye(6,6);
+
+//Output
+//
+//Maximum Number of Iterations Exceeded. Output may not be optimal.
+// lambda  =
+// 
+//   lower: [0x0 constant]
+//   upper: [0x0 constant]
+//   eqlin: [0x0 constant]
+//   ineqlin: [0x0 constant]
+// output  =
+// 
+//   Iterations: 1
+// exitflag  =
+// 
+//  1  
+// fopt  =
+// 
+//  - 9.9784864  
+// xopt  =
+// 
+//    0.9103357  
+//  - 0.1104630  
+//    0.0476471  
+//  - 2.0070896  
+//    0.2116560  
+//  - 1.1624291 
+[xopt,fopt,exitflag,output,lambda]=qpipoptmat(H,f,A,b,Aeq,beq,lb,ub,[],param)
+   
diff --git a/tests/general_tests/qpipoptmat/qpipoptmat_zeros.sce b/tests/general_tests/qpipoptmat/qpipoptmat_zeros.sce
new file mode 100644
index 0000000..ee9cfc9
--- /dev/null
+++ b/tests/general_tests/qpipoptmat/qpipoptmat_zeros.sce
@@ -0,0 +1,35 @@
+// A simple example without constraints
+
+f=[0]'; 
+H =[0];
+
+//Output
+//
+//Optimal Solution Found.
+// 
+//   lower: 0
+//   upper: 0
+//   eqlin: [0x0 constant]
+//   ineqlin: [0x0 constant]
+// 
+// lambda   
+// 
+//   Iterations: 0
+// 
+// output   
+// 
+//  0  
+// 
+// exitflag   
+// 
+//    0.  
+// 
+// fopt   
+// 
+//    0.  
+// 
+// xopt 
+
+
+[xopt,fopt,exitflag,output,lambda]=qpipoptmat(H,f);
+
diff --git a/tests/general_tests/symphony/symphony_logical2.sce b/tests/general_tests/symphony/symphony_logical2.sce
index 5d0989f..9f34cc0 100644
--- a/tests/general_tests/symphony/symphony_logical2.sce
+++ b/tests/general_tests/symphony/symphony_logical2.sce
@@ -12,9 +12,9 @@ ub = repmat(%inf,3,1);
 
 // Constraint Matrix
 A = [3,2,5;
-             2,1,1;
-             1,1,3;
-             5,2,4]
+     2,1,1;
+     1,1,3;
+     5,2,4]
 
 // Lower Bound of constrains
 conlb = repmat(-%inf,4,1)
diff --git a/tests/general_tests/symphony/symphony_zeros.sce b/tests/general_tests/symphony/symphony_zeros.sce
new file mode 100644
index 0000000..a6615f6
--- /dev/null
+++ b/tests/general_tests/symphony/symphony_zeros.sce
@@ -0,0 +1,46 @@
+// Check for size of Objective Coefficient
+// A basic case :
+
+// Objective function
+c = [0 0 0]';
+
+// Lower Bound of variable
+lb = [];
+
+// Upper Bound of variables
+ub = [];
+
+// Constraint Matrix
+A = []
+
+// Lower Bound of constrains
+conlb = []
+
+// Upper Bound of constrains
+conub = []
+
+// Row Matrix for telling symphony that the is integer or not
+isInt = [repmat(%f,1,3)];
+
+// Output
+//Problem loaded into environment.
+//Note: There is no limit on time.
+//An optimal solution has been found.
+// output  =
+// 
+//   Iterations: 1
+// status  =
+// 
+//    227.  
+// f  =
+// 
+//    0.  
+// x  =
+// 
+//    0.  
+//    0.  
+//    0.
+
+// Calling Symphony
+[x,f,status,output] = symphony(3,0,c,isInt,lb,ub,A,conlb,conub,-1)
+
diff --git a/thirdparty/linux/include/coin/.IpIpoptCalculatedQuantities.hpp.swp b/thirdparty/linux/include/coin/.IpIpoptCalculatedQuantities.hpp.swp
new file mode 100644
index 0000000..3e69b30
Binary files /dev/null and b/thirdparty/linux/include/coin/.IpIpoptCalculatedQuantities.hpp.swp differ
-- 
cgit