From 79583a44468943fad22ba1de2dd25dd86f7be167 Mon Sep 17 00:00:00 2001 From: Harpreet Date: Tue, 22 Dec 2015 14:51:05 +0530 Subject: Bugs by prof fixed 2 --- help/en_US/lsqlin.xml | 23 +-- help/en_US/lsqnonneg.xml | 13 +- help/en_US/master_help.xml | 4 - help/en_US/qpipopt.xml | 8 +- help/en_US/qpipopt_mat.xml | 142 --------------- help/en_US/qpipoptmat.xml | 68 ++++--- help/en_US/scilab_en_US_help/JavaHelpSearch/DOCS | Bin 7489 -> 7496 bytes .../scilab_en_US_help/JavaHelpSearch/DOCS.TAB | Bin 862 -> 868 bytes .../en_US/scilab_en_US_help/JavaHelpSearch/OFFSETS | Bin 273 -> 270 bytes .../scilab_en_US_help/JavaHelpSearch/POSITIONS | Bin 36150 -> 36157 bytes help/en_US/scilab_en_US_help/JavaHelpSearch/SCHEMA | 2 +- help/en_US/scilab_en_US_help/JavaHelpSearch/TMAP | Bin 16384 -> 16384 bytes .../scilab_en_US_help/_LaTeX_lsqlin.xml_1.png | Bin 3026 -> 3129 bytes .../scilab_en_US_help/_LaTeX_qpipoptmat.xml_1.png | Bin 3110 -> 3180 bytes .../scilab_en_US_help/_LaTeX_symphony.xml_1.png | Bin 2934 -> 3468 bytes .../scilab_en_US_help/_LaTeX_symphonymat.xml_1.png | Bin 2547 -> 3160 bytes help/en_US/scilab_en_US_help/index.html | 12 -- help/en_US/scilab_en_US_help/jhelpmap.jhm | 2 - help/en_US/scilab_en_US_help/jhelptoc.xml | 2 - help/en_US/scilab_en_US_help/lsqlin.html | 22 ++- help/en_US/scilab_en_US_help/lsqnonneg.html | 12 +- help/en_US/scilab_en_US_help/qpipopt.html | 13 +- help/en_US/scilab_en_US_help/qpipoptmat.html | 59 +++--- .../section_19f4f1e5726c01d683e8b82be0a7e910.html | 12 -- help/en_US/scilab_en_US_help/symphony.html | 15 +- help/en_US/scilab_en_US_help/symphonymat.html | 15 +- help/en_US/symphony.xml | 13 +- help/en_US/symphony_mat.xml | 202 --------------------- help/en_US/symphonymat.xml | 13 +- 29 files changed, 140 insertions(+), 512 deletions(-) delete mode 100644 help/en_US/qpipopt_mat.xml delete mode 100644 help/en_US/symphony_mat.xml (limited to 'help') diff --git a/help/en_US/lsqlin.xml b/help/en_US/lsqlin.xml index 1216bae..1936e11 100644 --- a/help/en_US/lsqlin.xml +++ b/help/en_US/lsqlin.xml @@ -24,11 +24,11 @@ Calling Sequence - x = lsqlin(C,d,A,b) - x = lsqlin(C,d,A,b,Aeq,beq) - x = lsqlin(C,d,A,b,Aeq,beq,lb,ub) - x = lsqlin(C,d,A,b,Aeq,beq,lb,ub,x0) - x = lsqlin(C,d,A,b,Aeq,beq,lb,ub,x0,param) + xopt = lsqlin(C,d,A,b) + xopt = lsqlin(C,d,A,b,Aeq,beq) + xopt = lsqlin(C,d,A,b,Aeq,beq,lb,ub) + xopt = lsqlin(C,d,A,b,Aeq,beq,lb,ub,x0) + xopt = lsqlin(C,d,A,b,Aeq,beq,lb,ub,x0,param) [xopt,resnorm,residual,exitflag,output,lambda] = lsqlin( ... ) @@ -66,9 +66,9 @@ exitflag : Integer identifying the reason the algorithm terminated. output : - Structure containing information about the optimization. + Structure containing information about the optimization. Right now it contains number of iteration. lambda : - Structure containing the Lagrange multipliers at the solution x (separated by constraint type). + Structure containing the Lagrange multipliers at the solution x (separated by constraint type).It contains lower, upper and linear equality, inequality constraints. @@ -82,14 +82,14 @@ Search the minimum of a constrained linear least square problem specified by : \begin{eqnarray} &\mbox{min}_{x} & 1/2||C*x - d||_2^2 \\ -& \text{subject to} & A.x \leq b \\ -& & Aeq.x \leq beq \\ +& \text{subject to} & A*x \leq b \\ +& & Aeq*x = beq \\ & & lb \leq x \leq ub \\ \end{eqnarray} -We are calling IPOpt for solving the linear least square problem, IPOpt is a library written in C++. The code has been written by ​Andreas Wächter and ​Carl Laird. +We are calling IPOpt for solving the linear least square problem, IPOpt is a library written in C++. @@ -116,6 +116,7 @@ b = [0.5251 0.2026 0.6721]; [xopt,resnorm,residual,exitflag,output,lambda] = lsqlin(C,d,A,b) +// Press ENTER to continue ]]> @@ -123,6 +124,7 @@ b = [0.5251 Examples diff --git a/help/en_US/lsqnonneg.xml b/help/en_US/lsqnonneg.xml index 95c8da1..daf79bf 100644 --- a/help/en_US/lsqnonneg.xml +++ b/help/en_US/lsqnonneg.xml @@ -24,8 +24,8 @@ Calling Sequence - x = lsqnonneg(C,d) - x = lsqnonneg(C,d,param) + xopt = lsqnonneg(C,d) + xopt = lsqnonneg(C,d,param) [xopt,resnorm,residual,exitflag,output,lambda] = lsqnonneg( ... ) @@ -47,9 +47,9 @@ exitflag : Integer identifying the reason the algorithm terminated. output : - Structure containing information about the optimization. + Structure containing information about the optimization. Right now it contains number of iteration. lambda : - Structure containing the Lagrange multipliers at the solution x (separated by constraint type). + Structure containing the Lagrange multipliers at the solution x (separated by constraint type).It contains lower, upper and linear equality, inequality constraints. @@ -68,7 +68,7 @@ Solves nonnegative least-squares curve fitting problems specified by : -We are calling IPOpt for solving the nonnegative least-squares curve fitting problems, IPOpt is a library written in C++. The code has been written by ​Andreas Wächter and ​Carl Laird. +We are calling IPOpt for solving the nonnegative least-squares curve fitting problems, IPOpt is a library written in C++. @@ -77,7 +77,7 @@ We are calling IPOpt for solving the nonnegative least-squares curve fitting pro Examples diff --git a/help/en_US/master_help.xml b/help/en_US/master_help.xml index 999a2d7..e59ac6c 100644 --- a/help/en_US/master_help.xml +++ b/help/en_US/master_help.xml @@ -4,10 +4,8 @@ - - @@ -86,10 +84,8 @@ &a3d4ec65684b561d91f7a255acd23f51c; &aa4a031935f5eed6cfc8fc4a49823b00b; &a6b85f6e0c98751f20b68663a23cb4cd2; -&a44928acec52adf395379e18fcff06730; &a8549a3935858ed104f4749ca2243456a; &aca972f273143ecb39f56b42e4723ac67; -&a9953e61e8dd264a86df73772d3055e7f; &a9910ada35b57b0581e8a77d145abac4a; Symphony Native Functions diff --git a/help/en_US/qpipopt.xml b/help/en_US/qpipopt.xml index c0756f8..d9a0e6e 100644 --- a/help/en_US/qpipopt.xml +++ b/help/en_US/qpipopt.xml @@ -64,9 +64,9 @@ exitflag : Integer identifying the reason the algorithm terminated. output : - Structure containing information about the optimization. + Structure containing information about the optimization. Right now it contains number of iteration. lambda : - Structure containing the Lagrange multipliers at the solution x (separated by constraint type). + Structure containing the Lagrange multipliers at the solution x (separated by constraint type).It contains lower, upper and linear equality, inequality constraints. @@ -87,7 +87,7 @@ find the minimum of f(x) such that -We are calling IPOpt for solving the quadratic problem, IPOpt is a library written in C++. The code has been written by ​Andreas Wächter and ​Carl Laird. +We are calling IPOpt for solving the quadratic problem, IPOpt is a library written in C++. @@ -113,6 +113,7 @@ nbCon = 5; x0 = repmat(0,nbVar,1); param = list("MaxIter", 300, "CpuTime", 100); [xopt,fopt,exitflag,output,lambda]=qpipopt(nbVar,nbCon,Q,p,lb,ub,conMatrix,conLB,conUB,x0,param) +// Press ENTER to continue ]]> @@ -137,7 +138,6 @@ ub = [%inf; %inf]; nbVar = 2; nbCon = 3; [xopt,fopt,exitflag,output,lambda] = qpipopt(nbVar,nbCon,Q,p,lb,ub,conMatrix,conLB,conUB) - ]]> diff --git a/help/en_US/qpipopt_mat.xml b/help/en_US/qpipopt_mat.xml deleted file mode 100644 index 7dec2b1..0000000 --- a/help/en_US/qpipopt_mat.xml +++ /dev/null @@ -1,142 +0,0 @@ - - - - - - - - qpipopt_mat - Solves a linear quadratic problem. - - - - - Calling Sequence - - xopt = qpipopt_mat(nbVar,nbCon,Q,p,LB,UB,conMatrix,conLB,conUB) - x = qpipopt_mat(H,f) - x = qpipopt_mat(H,f,A,b) - x = qpipopt_mat(H,f,A,b,Aeq,beq) - x = qpipopt_mat(H,f,A,b,Aeq,beq,lb,ub) - [xopt,fopt,exitflag,output,lamda] = qpipopt_mat( ... ) - - - - - - Parameters - - H : - a n x n matrix of doubles, where n is number of variables, represents coefficients of quadratic in the quadratic problem. - f : - a n x 1 matrix of doubles, where n is number of variables, represents coefficients of linear in the quadratic problem - A : - a m x n matrix of doubles, represents the linear coefficients in the inequality constraints - b : - a column vector of doubles, represents the linear coefficients in the inequality constraints - Aeq : - a meq x n matrix of doubles, represents the linear coefficients in the equality constraints - beq : - a vector of doubles, represents the linear coefficients in the equality constraints - LB : - a n x 1 matrix of doubles, where n is number of variables, contains lower bounds of the variables. - UB : - a n x 1 matrix of doubles, where n is number of variables, contains upper bounds of the variables. - xopt : - a nx1 matrix of doubles, the computed solution of the optimization problem. - fopt : - a 1x1 matrix of doubles, the function value at x. - exitflag : - Integer identifying the reason the algorithm terminated. - output : - Structure containing information about the optimization. - lambda : - Structure containing the Lagrange multipliers at the solution x (separated by constraint type). - - - - - Description - -Search the minimum of a constrained linear quadratic optimization problem specified by : -find the minimum of f(x) such that - - - -\begin{eqnarray} -&\mbox{min}_{x} -& 1/2*x'*H*x + f'*x \\ -& \text{subject to} & A.x \leq b \\ -& & Aeq.x \leq beq \\ -& & lb \leq x \leq ub \\ -\end{eqnarray} - - - -We are calling IPOpt for solving the quadratic problem, IPOpt is a library written in C++. The code has been written by ​Andreas Wächter and ​Carl Laird. - - - - - - - Examples - - - - - Examples - - - - - Authors - - Keyur Joshi, Saikiran, Iswarya, Harpreet Singh - - - diff --git a/help/en_US/qpipoptmat.xml b/help/en_US/qpipoptmat.xml index f3830f4..2ea714d 100644 --- a/help/en_US/qpipoptmat.xml +++ b/help/en_US/qpipoptmat.xml @@ -24,12 +24,12 @@ Calling Sequence - x = qpipoptmat(H,f) - x = qpipoptmat(H,f,A,b) - x = qpipoptmat(H,f,A,b,Aeq,beq) - x = qpipoptmat(H,f,A,b,Aeq,beq,lb,ub) - x = qpipoptmat(H,f,A,b,Aeq,beq,lb,ub,x0) - x = qpipoptmat(H,f,A,b,Aeq,beq,lb,ub,x0,param) + xopt = qpipoptmat(H,f) + xopt = qpipoptmat(H,f,A,b) + xopt = qpipoptmat(H,f,A,b,Aeq,beq) + xopt = qpipoptmat(H,f,A,b,Aeq,beq,lb,ub) + xopt = qpipoptmat(H,f,A,b,Aeq,beq,lb,ub,x0) + xopt = qpipoptmat(H,f,A,b,Aeq,beq,lb,ub,x0,param) [xopt,fopt,exitflag,output,lamda] = qpipoptmat( ... ) @@ -65,9 +65,9 @@ exitflag : Integer identifying the reason the algorithm terminated. output : - Structure containing information about the optimization. + Structure containing information about the optimization. Right now it contains number of iteration. lambda : - Structure containing the Lagrange multipliers at the solution x (separated by constraint type). + Structure containing the Lagrange multipliers at the solution x (separated by constraint type).It contains lower, upper and linear equality, inequality constraints. @@ -82,43 +82,19 @@ find the minimum of f(x) such that \begin{eqnarray} &\mbox{min}_{x} & 1/2*x'*H*x + f'*x \\ -& \text{subject to} & A.x \leq b \\ -& & Aeq.x \leq beq \\ +& \text{subject to} & A*x \leq b \\ +& & Aeq*x = beq \\ & & lb \leq x \leq ub \\ \end{eqnarray} -We are calling IPOpt for solving the quadratic problem, IPOpt is a library written in C++. The code has been written by ​Andreas Wächter and ​Carl Laird. +We are calling IPOpt for solving the quadratic problem, IPOpt is a library written in C++. - - Examples - - - Examples + + + Examples + diff --git a/help/en_US/scilab_en_US_help/JavaHelpSearch/DOCS b/help/en_US/scilab_en_US_help/JavaHelpSearch/DOCS index 2aa9c2c..9b6386a 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 954ffd9..8f3ddaf 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 d5a0fd6..d668ed6 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 1fbf883..65379cd 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 93aea58..b5697c6 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=1435 id2=1 +TMAP bs=2048 rt=1 fl=-1 id1=1439 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 141985f..e2f089a 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/_LaTeX_lsqlin.xml_1.png b/help/en_US/scilab_en_US_help/_LaTeX_lsqlin.xml_1.png index d89b104..873dc47 100644 Binary files a/help/en_US/scilab_en_US_help/_LaTeX_lsqlin.xml_1.png and b/help/en_US/scilab_en_US_help/_LaTeX_lsqlin.xml_1.png differ diff --git a/help/en_US/scilab_en_US_help/_LaTeX_qpipoptmat.xml_1.png b/help/en_US/scilab_en_US_help/_LaTeX_qpipoptmat.xml_1.png index b6e2743..7331197 100644 Binary files a/help/en_US/scilab_en_US_help/_LaTeX_qpipoptmat.xml_1.png and b/help/en_US/scilab_en_US_help/_LaTeX_qpipoptmat.xml_1.png differ diff --git a/help/en_US/scilab_en_US_help/_LaTeX_symphony.xml_1.png b/help/en_US/scilab_en_US_help/_LaTeX_symphony.xml_1.png index 07dafd6..96b5161 100644 Binary files a/help/en_US/scilab_en_US_help/_LaTeX_symphony.xml_1.png and b/help/en_US/scilab_en_US_help/_LaTeX_symphony.xml_1.png differ diff --git a/help/en_US/scilab_en_US_help/_LaTeX_symphonymat.xml_1.png b/help/en_US/scilab_en_US_help/_LaTeX_symphonymat.xml_1.png index 2e81ca1..94c5200 100644 Binary files a/help/en_US/scilab_en_US_help/_LaTeX_symphonymat.xml_1.png and b/help/en_US/scilab_en_US_help/_LaTeX_symphonymat.xml_1.png differ diff --git a/help/en_US/scilab_en_US_help/index.html b/help/en_US/scilab_en_US_help/index.html index c942e96..03ce98c 100644 --- a/help/en_US/scilab_en_US_help/index.html +++ b/help/en_US/scilab_en_US_help/index.html @@ -50,12 +50,6 @@ -
  • qpipopt_mat — Solves a linear quadratic problem.
    • - - - - -
  • qpipoptmat — Solves a linear quadratic problem.
    • @@ -68,12 +62,6 @@ -
  • symphony_mat — Solves a mixed integer linear programming constrained optimization problem in intlinprog format.
    • - - - - -
  • symphonymat — Solves a mixed integer linear programming constrained optimization problem in intlinprog format.
  • Symphony Native Functions diff --git a/help/en_US/scilab_en_US_help/jhelpmap.jhm b/help/en_US/scilab_en_US_help/jhelpmap.jhm index f046f8a..0226c5e 100644 --- a/help/en_US/scilab_en_US_help/jhelpmap.jhm +++ b/help/en_US/scilab_en_US_help/jhelpmap.jhm @@ -6,10 +6,8 @@ - - diff --git a/help/en_US/scilab_en_US_help/jhelptoc.xml b/help/en_US/scilab_en_US_help/jhelptoc.xml index 7722be3..f53e713 100644 --- a/help/en_US/scilab_en_US_help/jhelptoc.xml +++ b/help/en_US/scilab_en_US_help/jhelptoc.xml @@ -6,10 +6,8 @@ - - diff --git a/help/en_US/scilab_en_US_help/lsqlin.html b/help/en_US/scilab_en_US_help/lsqlin.html index bf5a259..b371871 100644 --- a/help/en_US/scilab_en_US_help/lsqlin.html +++ b/help/en_US/scilab_en_US_help/lsqlin.html @@ -37,11 +37,11 @@

    Calling Sequence

    - 
    x = lsqlin(C,d,A,b)
-x = lsqlin(C,d,A,b,Aeq,beq)
-x = lsqlin(C,d,A,b,Aeq,beq,lb,ub)
-x = lsqlin(C,d,A,b,Aeq,beq,lb,ub,x0)
-x = lsqlin(C,d,A,b,Aeq,beq,lb,ub,x0,param)
+   
    xopt = lsqlin(C,d,A,b)
+xopt = lsqlin(C,d,A,b,Aeq,beq)
+xopt = lsqlin(C,d,A,b,Aeq,beq,lb,ub)
+xopt = lsqlin(C,d,A,b,Aeq,beq,lb,ub,x0)
+xopt = lsqlin(C,d,A,b,Aeq,beq,lb,ub,x0,param)
 [xopt,resnorm,residual,exitflag,output,lambda] = lsqlin( ... )

    Parameters

    @@ -74,14 +74,14 @@
    exitflag :

    Integer identifying the reason the algorithm terminated.

    output : -

    Structure containing information about the optimization.

    +

    Structure containing information about the optimization. Right now it contains number of iteration.

    lambda : -

    Structure containing the Lagrange multipliers at the solution x (separated by constraint type).

    +

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

    Description

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

    -

    We are calling IPOpt for solving the linear least square problem, IPOpt is a library written in C++. The code has been written by ​Andreas Wächter and ​Carl Laird.

    +

    We are calling IPOpt for solving the linear least square problem, IPOpt is a library written in C++.

    Examples

    @@ -102,10 +102,12 @@ b = [0.5251 0.2026 0.6721]; -[xopt,resnorm,residual,exitflag,output,lambda] = lsqlin(C,d,A,b)
    +[xopt,resnorm,residual,exitflag,output,lambda] = lsqlin(C,d,A,b) +// Press ENTER to continue

    Examples

    - 
    C = [0.9501    0.7620    0.6153    0.4057
+   
    //A basic example for equality, inequality and 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
diff --git a/help/en_US/scilab_en_US_help/lsqnonneg.html b/help/en_US/scilab_en_US_help/lsqnonneg.html
index 4f2f661..40139a0 100644
--- a/help/en_US/scilab_en_US_help/lsqnonneg.html
+++ b/help/en_US/scilab_en_US_help/lsqnonneg.html
@@ -37,8 +37,8 @@
 
 
 
    Calling Sequence
    
-   
    x = lsqnonneg(C,d)
-x = lsqnonneg(C,d,param)
+   
    xopt = lsqnonneg(C,d)
+xopt = lsqnonneg(C,d,param)
 [xopt,resnorm,residual,exitflag,output,lambda] = lsqnonneg( ... )
    
 
 
    Parameters
    
@@ -55,18 +55,18 @@
    
    exitflag :
       
    Integer identifying the reason the algorithm terminated.
    
    
    output :
-      
    Structure containing information about the optimization.
    
+      
    Structure containing information about the optimization. Right now it contains number of iteration.
    
    
    lambda :
-      
    Structure containing the Lagrange multipliers at the solution x (separated by constraint type).
    
+      
    Structure containing the Lagrange multipliers at the solution x (separated by constraint type).It contains lower, upper and linear equality, inequality constraints.
    
 
 
    Description
    
    
    Solves nonnegative least-squares curve fitting problems specified by :
    
    
    
-   
    We are calling IPOpt for solving the nonnegative least-squares curve fitting problems, IPOpt is a library written in C++. The code has been written by ​Andreas Wächter and ​Carl Laird.
    
+   
    We are calling IPOpt for solving the nonnegative least-squares curve fitting problems, IPOpt is a library written in C++.
    
    
    
 
 
    Examples
    
-   
    A basic lsqnonneg problem
+   
    // A basic lsqnonneg problem
 C = [
 0.0372    0.2869
 0.6861    0.7071
diff --git a/help/en_US/scilab_en_US_help/qpipopt.html b/help/en_US/scilab_en_US_help/qpipopt.html
index 7588c1d..7cc0560 100644
--- a/help/en_US/scilab_en_US_help/qpipopt.html
+++ b/help/en_US/scilab_en_US_help/qpipopt.html
@@ -20,7 +20,7 @@
 
       
    		
       
-      	qpipopt_mat >>
+      	qpipoptmat >>
 
       
     
    
@@ -72,15 +72,15 @@
    
    exitflag :
       
    Integer identifying the reason the algorithm terminated.
    
    
    output :
-      
    Structure containing information about the optimization.
    
+      
    Structure containing information about the optimization. Right now it contains number of iteration.
    
    
    lambda :
-      
    Structure containing the Lagrange multipliers at the solution x (separated by constraint type).
    
+      
    Structure containing the Lagrange multipliers at the solution x (separated by constraint type).It contains lower, upper and linear equality, inequality constraints.
    
 
 
    Description
    
    
    Search the minimum of a constrained linear quadratic optimization problem specified by :
 find the minimum of f(x) such that
    
    
    
-   
    We are calling IPOpt for solving the quadratic problem, IPOpt is a library written in C++. The code has been written by ​Andreas Wächter and ​Carl Laird.
    
+   
    We are calling IPOpt for solving the quadratic problem, IPOpt is a library written in C++.
    
    
    
 
 
    Examples
    
@@ -100,7 +100,8 @@ 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,Q,p,lb,ub,conMatrix,conLB,conUB,x0,param)
    
+[xopt,fopt,exitflag,output,lambda]=qpipopt(nbVar,nbCon,Q,p,lb,ub,conMatrix,conLB,conUB,x0,param)
+// Press ENTER to continue
    
 
 
    Examples
    
    
    //Find the value of x that minimize following function
@@ -138,7 +139,7 @@ find the minimum of f(x) such that
    
 
       
    		
       
-      	qpipopt_mat >>
+      	qpipoptmat >>
 
       
     
    
diff --git a/help/en_US/scilab_en_US_help/qpipoptmat.html b/help/en_US/scilab_en_US_help/qpipoptmat.html
index e1d301a..8b81cac 100644
--- a/help/en_US/scilab_en_US_help/qpipoptmat.html
+++ b/help/en_US/scilab_en_US_help/qpipoptmat.html
@@ -12,7 +12,7 @@
     
    
     
    
       
-    	<< qpipopt_mat
+    	<< qpipopt
 
       
       
@@ -37,12 +37,12 @@
 
 
 
    Calling Sequence
    
-   
    x = qpipoptmat(H,f)
-x = qpipoptmat(H,f,A,b)
-x = qpipoptmat(H,f,A,b,Aeq,beq)
-x = qpipoptmat(H,f,A,b,Aeq,beq,lb,ub)
-x = qpipoptmat(H,f,A,b,Aeq,beq,lb,ub,x0)
-x = qpipoptmat(H,f,A,b,Aeq,beq,lb,ub,x0,param)
+   
    xopt = qpipoptmat(H,f)
+xopt = qpipoptmat(H,f,A,b)
+xopt = qpipoptmat(H,f,A,b,Aeq,beq)
+xopt = qpipoptmat(H,f,A,b,Aeq,beq,lb,ub)
+xopt = qpipoptmat(H,f,A,b,Aeq,beq,lb,ub,x0)
+xopt = qpipoptmat(H,f,A,b,Aeq,beq,lb,ub,x0,param)
 [xopt,fopt,exitflag,output,lamda] = qpipoptmat( ... )
    
 
 
    Parameters
    
@@ -73,20 +73,36 @@
    
    exitflag :
       
    Integer identifying the reason the algorithm terminated.
    
    
    output :
-      
    Structure containing information about the optimization.
    
+      
    Structure containing information about the optimization. Right now it contains number of iteration.
    
    
    lambda :
-      
    Structure containing the Lagrange multipliers at the solution x (separated by constraint type).
    
+      
    Structure containing the Lagrange multipliers at the solution x (separated by constraint type).It contains lower, upper and linear equality, inequality constraints.
    
 
 
    Description
    
    
    Search the minimum of a constrained linear quadratic optimization problem specified by :
 find the minimum of f(x) such that
    
    
    
-   
    We are calling IPOpt for solving the quadratic problem, IPOpt is a library written in C++. The code has been written by ​Andreas Wächter and ​Carl Laird.
    
+   
    We are calling IPOpt for solving the quadratic problem, IPOpt is a library written in C++.
    
    
    
 
 
    Examples
    
-   
    //Find x in R^6 such that:
+   
    //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];
+lb = [0; 0];
+ub = [%inf; %inf];
+[xopt,fopt,exitflag,output,lambda] = qpipoptmat(H,f,A,b,[],[],lb,ub)
+// Press ENTER to continue
    
 
+
    Examples
    
+   
    //Find x in R^6 such that:
 Aeq= [1,-1,1,0,3,1;
 -1,0,-3,-4,5,6;
 2,5,3,0,1,0];
@@ -100,24 +116,7 @@ find the minimum of f(x) such that
    
 param = list("MaxIter", 300, "CpuTime", 100);
 //and minimize 0.5*x'*Q*x + p'*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)
-clear H f A b Aeq beq lb ub;
    
-
-
    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];
-lb = [0; 0];
-ub = [%inf; %inf];
-[xopt,fopt,exitflag,output,lambda] = qpipoptmat(H,f,A,b,[],[],lb,ub)
    
+[xopt,fopt,exitflag,output,lambda]=qpipoptmat(H,f,A,b,Aeq,beq,lb,ub,[],param)
    
 
 
    Authors
    
    
    • Keyur Joshi, Saikiran, Iswarya, Harpreet Singh
    
@@ -128,7 +127,7 @@ find the minimum of f(x) such that
    
     
    Report an issue
    
 
    
       
-    	<< qpipopt_mat
+    	<< qpipopt
 
       
       
diff --git a/help/en_US/scilab_en_US_help/section_19f4f1e5726c01d683e8b82be0a7e910.html b/help/en_US/scilab_en_US_help/section_19f4f1e5726c01d683e8b82be0a7e910.html
index 7219261..a79bad0 100644
--- a/help/en_US/scilab_en_US_help/section_19f4f1e5726c01d683e8b82be0a7e910.html
+++ b/help/en_US/scilab_en_US_help/section_19f4f1e5726c01d683e8b82be0a7e910.html
@@ -49,12 +49,6 @@
 
 
 
-
  • qpipopt_mat — Solves a linear quadratic problem.
    • 
-
-
-
-
-
 
  • qpipoptmat — Solves a linear quadratic problem.
    • 
 
 
@@ -67,12 +61,6 @@
 
 
 
-
  • symphony_mat — Solves a mixed integer linear programming constrained optimization problem in intlinprog format.
    • 
-
-
-
-
-
 
  • symphonymat — Solves a mixed integer linear programming constrained optimization problem in intlinprog format.
    • 
 
 
  • Symphony Native Functions
diff --git a/help/en_US/scilab_en_US_help/symphony.html b/help/en_US/scilab_en_US_help/symphony.html
index 14e9118..9b2bebe 100644
--- a/help/en_US/scilab_en_US_help/symphony.html
+++ b/help/en_US/scilab_en_US_help/symphony.html
@@ -20,7 +20,7 @@
 
       
    		• 
       
-      	symphony_mat >>
+      	symphonymat >>
 
       
     
    
@@ -72,13 +72,13 @@
    
    status :
       
    status flag from symphony.
    
    
    output :
-      
    The output data structure contains detailed informations about the optimization process.
    
+      
    The output data structure contains detailed informations about the optimization process. Right now it contains number of iteration.
    
 
 
    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
    
-   
    
-   
    We are calling SYMPHONY written in C by gateway files for the actual computation. SYMPHONY was originally written by ​Ted Ralphs, ​Menal Guzelsoy and ​Ashutosh Mahajan.
    
+   
    
+   
    We are calling SYMPHONY written in C by gateway files for the actual computation.
    
    
    
 
 
    Examples
    
@@ -102,7 +102,8 @@ find the minimum or maximum of f(x) such that
    
 xopt = [1 1 0 1 7.25 0 0.25 3.5]
 fopt = [8495]
 // Calling Symphony
-[x,f,status,output] = symphony(8,3,c,isInt,lb,ub,conMatrix,conlb,conub,1)
    +[x,f,status,output] = symphony(8,3,c,isInt,lb,ub,conMatrix,conlb,conub,1) +// Press ENTER to continue

    Examples

    // An advanced case where we set some options in symphony
@@ -186,7 +187,7 @@ find the minimum or maximum of f(x) such that
    
 // Optimal value
 fopt = [ 24381 ]
 // Calling Symphony
-[x,f,status,output] = symphony(nbVar,nbCon,p,isInt,lb,ub,conMatrix,conLB,conUB,-1,options)
    +[x,f,status,output] = symphony(nbVar,nbCon,p,isInt,lb,ub,conMatrix,conLB,conUB,-1,options);

    Authors

    • Keyur Joshi, Saikiran, Iswarya, Harpreet Singh
    @@ -205,7 +206,7 @@ find the minimum or maximum of f(x) such that

    - symphony_mat >> + symphonymat >> diff --git a/help/en_US/scilab_en_US_help/symphonymat.html b/help/en_US/scilab_en_US_help/symphonymat.html index 2e89728..611010b 100644 --- a/help/en_US/scilab_en_US_help/symphonymat.html +++ b/help/en_US/scilab_en_US_help/symphonymat.html @@ -12,7 +12,7 @@
    - << symphony_mat + << symphony @@ -69,13 +69,13 @@
    status :

    status flag from symphony.

    output : -

    The output data structure contains detailed informations about the optimization process.

    +

    The output data structure contains detailed informations about the optimization process. Right now it contains number of iteration.

    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

    -

    -

    We are calling SYMPHONY written in C by gateway files for the actual computation. SYMPHONY was originally written by ​Ted Ralphs, ​Menal Guzelsoy and ​Ashutosh Mahajan.

    +

    +

    We are calling SYMPHONY written in C by gateway files for the actual computation.

    Examples

    @@ -92,7 +92,8 @@ find the minimum or maximum of f(x) such that

    beq = [ 25, 1.25, 1.25] intcon = [1 2 3 4]; // Calling Symphony -[x,f,status,output] = symphonymat(c,intcon,[],[],Aeq,beq,lb,ub)
    +[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
@@ -154,7 +155,7 @@ find the minimum or maximum of f(x) such that
    
 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(objCoef,2)
+nbVar = size(objCoef,1)
 conUB=[11927 13727 11551 13056 13460 ];
 // Lower Bound of variables
 lb = repmat(0,1,nbVar)
@@ -185,7 +186,7 @@ find the minimum or maximum of f(x) such that
    Report an issue
    - << symphony_mat + << symphony diff --git a/help/en_US/symphony.xml b/help/en_US/symphony.xml index a80f022..9fb615d 100644 --- a/help/en_US/symphony.xml +++ b/help/en_US/symphony.xml @@ -64,7 +64,7 @@ status : status flag from symphony. output : - The output data structure contains detailed informations about the optimization process. + The output data structure contains detailed informations about the optimization process. Right now it contains number of iteration. @@ -78,14 +78,15 @@ find the minimum or maximum of f(x) such that \begin{eqnarray} &\mbox{min}_{x} -& f(x) \\ -& \text{subject to} & conLB \leq C(x) \leq conUB \\ +& f^T*x \\ +& \text{subject to} & conLB \leq C*x \leq conUB \\ & & lb \leq x \leq ub \\ +& & x_i \in \!\, \mathbb{Z}, i \in \!\, I \end{eqnarray} -We are calling SYMPHONY written in C by gateway files for the actual computation. SYMPHONY was originally written by ​Ted Ralphs, ​Menal Guzelsoy and ​Ashutosh Mahajan. +We are calling SYMPHONY written in C by gateway files for the actual computation. @@ -115,6 +116,7 @@ xopt = [1 1 0 1 7.25 0 0.25 3.5] fopt = [8495] // Calling Symphony [x,f,status,output] = symphony(8,3,c,isInt,lb,ub,conMatrix,conlb,conub,1) +// Press ENTER to continue ]]> @@ -203,8 +205,7 @@ 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 .. // Optimal value fopt = [ 24381 ] // Calling Symphony -[x,f,status,output] = symphony(nbVar,nbCon,p,isInt,lb,ub,conMatrix,conLB,conUB,-1,options) - +[x,f,status,output] = symphony(nbVar,nbCon,p,isInt,lb,ub,conMatrix,conLB,conUB,-1,options); ]]> diff --git a/help/en_US/symphony_mat.xml b/help/en_US/symphony_mat.xml deleted file mode 100644 index 4f6e9c9..0000000 --- a/help/en_US/symphony_mat.xml +++ /dev/null @@ -1,202 +0,0 @@ - - - - - - - - symphony_mat - Solves a mixed integer linear programming constrained optimization problem in intlinprog format. - - - - - Calling Sequence - - xopt = symphony_mat(f,intcon,A,b) - xopt = symphony_mat(f,intcon,A,b,Aeq,beq) - xopt = symphony_mat(f,intcon,A,b,Aeq,beq,lb,ub) - xopt = symphony_mat(f,intcon,A,b,Aeq,beq,lb,ub,options) - [xopt,fopt,status,output] = symphony_mat( ... ) - - - - - - Parameters - - f : - a 1xn matrix of doubles, where n is number of variables, 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 doubles. A represents the linear coefficients in the constraints A*x ≤ b. A has size M-by-N, where M is the number of constraints and N is number of variables - b : - Linear inequality constraint vector, specified as a vector of doubles. b represents the constant vector in the constraints A*x ≤ b. b has length M, where A is M-by-N - Aeq : - Linear equality constraint matrix, specified as a matrix of doubles. Aeq represents the linear coefficients in the constraints Aeq*x = beq. Aeq has size Meq-by-N, where Meq is the number of constraints and N is number of variables - beq : - Linear equality constraint vector, specified as a vector of doubles. beq represents the constant vector in the constraints Aeq*x = beq. beq has length Meq, where Aeq is Meq-by-N. - lb : - Lower bounds, specified as a vector or array of doubles. lb represents the lower bounds elementwise in lb ≤ x ≤ ub. - ub : - Upper bounds, specified as a vector or array of doubles. ub represents the upper bounds elementwise in lb ≤ x ≤ ub. - options : - a 1xq marix of string, provided to set the paramters in symphony - xopt : - a 1xn matrix of doubles, the computed solution of the optimization problem - fopt : - a 1x1 matrix of doubles, the function value at x - output : - The output data structure contains detailed informations about the optimization process. - - - - - 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} -&\mbox{min}_{x} -& f(x) \\ -& \text{subject to} & conLB \leq C(x) \leq conUB \\ -& & lb \leq x \leq ub \\ -\end{eqnarray} - - - -We are calling SYMPHONY written in C by gateway files for the actual computation. SYMPHONY was originally written by ​Ted Ralphs, ​Menal Guzelsoy and ​Ashutosh Mahajan. - - - - - - - Examples - - - - - Examples - - - - - Authors - - Keyur Joshi, Saikiran, Iswarya, Harpreet Singh - - - diff --git a/help/en_US/symphonymat.xml b/help/en_US/symphonymat.xml index d811582..ab2ca34 100644 --- a/help/en_US/symphonymat.xml +++ b/help/en_US/symphonymat.xml @@ -61,7 +61,7 @@ status : status flag from symphony. output : - The output data structure contains detailed informations about the optimization process. + The output data structure contains detailed informations about the optimization process. Right now it contains number of iteration. @@ -75,15 +75,16 @@ find the minimum or maximum of f(x) such that \begin{eqnarray} &\mbox{min}_{x} -& f(x) \\ -& \text{subject to} & A.x \leq b \\ -& & Aeq.x \leq beq \\ +& f^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} -We are calling SYMPHONY written in C by gateway files for the actual computation. SYMPHONY was originally written by ​Ted Ralphs, ​Menal Guzelsoy and ​Ashutosh Mahajan. +We are calling SYMPHONY written in C by gateway files for the actual computation. @@ -106,6 +107,7 @@ 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 ]]> @@ -193,7 +195,6 @@ 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 .. fopt = [ 24381 ] // Calling Symphony [x,f,status,output] = symphonymat(objCoef,intcon,conMatrix,conUB,[],[],lb,ub,options); - ]]> -- cgit