Bugs by prof fixed 2

12 files changed, 259 insertions, 259 deletions

diff --git a/macros/lsqlin.bin b/macros/lsqlin.bin
index 801025f..d7fccb3 100644
--- a/macros/lsqlin.bin
+++ b/macros/lsqlin.bin
diff --git a/macros/lsqlin.sci b/macros/lsqlin.sci
index 4a5fa2d..1dc1fd5 100644
--- a/macros/lsqlin.sci
+++ b/macros/lsqlin.sci
@@ -14,11 +14,11 @@ function [xopt,resnorm,residual,exitflag,output,lambda] = lsqlin (varargin)
 	// Solves a linear quadratic problem.
 	//
 	//   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
@@ -36,8 +36,8 @@ function [xopt,resnorm,residual,exitflag,output,lambda] = lsqlin (varargin)
 	//   resnorm : a double, objective value returned as the scalar value norm(C*x-d)^2.
 	//   residual : a vector of doubles, solution residuals returned as the vector C*x-d.
 	//   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).
+	//   output : 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).It contains lower, upper and linear equality, inequality constraints.
 	//   
 	//   Description
 	//   Search the minimum of a constrained linear least square problem specified by :
@@ -46,13 +46,13 @@ function [xopt,resnorm,residual,exitflag,output,lambda] = lsqlin (varargin)
 	//    \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}
 	//   </latex>
 	//   
-	//   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
 	// //A simple linear least square example
@@ -73,8 +73,10 @@ function [xopt,resnorm,residual,exitflag,output,lambda] = lsqlin (varargin)
 	//     0.2026
 	//     0.6721];
 	// [xopt,resnorm,residual,exitflag,output,lambda] = lsqlin(C,d,A,b)
+	// // Press ENTER to continue 
 	//    
-	// Examples 
+	// Examples
+	// //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
@@ -96,7 +98,6 @@ function [xopt,resnorm,residual,exitflag,output,lambda] = lsqlin (varargin)
 	// lb = -0.1*ones(4,1);
 	// ub = 2*ones(4,1);
 	// [xopt,resnorm,residual,exitflag,output,lambda] = lsqlin(C,d,A,b,Aeq,beq,lb,ub)
-	//
 	// Authors
 	// Harpreet Singh
 
diff --git a/macros/lsqnonneg.bin b/macros/lsqnonneg.bin
index cd8a04a..84e307b 100644
--- a/macros/lsqnonneg.bin
+++ b/macros/lsqnonneg.bin
diff --git a/macros/lsqnonneg.sci b/macros/lsqnonneg.sci
index 77e5e44..b8694b4 100644
--- a/macros/lsqnonneg.sci
+++ b/macros/lsqnonneg.sci
@@ -14,8 +14,8 @@ function [xopt,resnorm,residual,exitflag,output,lambda] = lsqnonneg (varargin)
 	// Solves nonnegative least-squares curve fitting problems.
 	//
 	//   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
@@ -25,8 +25,8 @@ function [xopt,resnorm,residual,exitflag,output,lambda] = lsqnonneg (varargin)
 	//   resnorm : a double, objective value returned as the scalar value norm(C*x-d)^2.
 	//   residual : a vector of doubles, solution residuals returned as the vector C*x-d.
 	//   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).
+	//   output : 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).It contains lower, upper and linear equality, inequality constraints.
 	//   
 	//   Description
 	//   Solves nonnegative least-squares curve fitting problems specified by :
@@ -39,10 +39,10 @@ function [xopt,resnorm,residual,exitflag,output,lambda] = lsqnonneg (varargin)
 	//    \end{eqnarray}
 	//   </latex>
 	//   
-	//   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
@@ -54,7 +54,6 @@ function [xopt,resnorm,residual,exitflag,output,lambda] = lsqnonneg (varargin)
 	//   	0.0747
 	//	    0.8405];
 	// [xopt,resnorm,residual,exitflag,output,lambda] = lsqnonneg(C,d)
-	//
 	// Authors
 	// Harpreet Singh
 
diff --git a/macros/qpipopt.bin b/macros/qpipopt.bin
index 2fd432e..584f327 100644
--- a/macros/qpipopt.bin
+++ b/macros/qpipopt.bin
diff --git a/macros/qpipopt.sci b/macros/qpipopt.sci
index 8b7cecd..affd061 100644
--- a/macros/qpipopt.sci
+++ b/macros/qpipopt.sci
@@ -34,8 +34,8 @@ function [xopt,fopt,exitflag,output,lambda] = qpipopt (varargin)
   //   xopt : a vector of doubles, the computed solution of the optimization problem.
   //   fopt : a double, 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).
+  //   output : 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).It contains lower, upper and linear equality, inequality constraints.
   //   
   //   Description
   //   Search the minimum of a constrained linear quadratic optimization problem specified by :
@@ -50,7 +50,7 @@ function [xopt,fopt,exitflag,output,lambda] = qpipopt (varargin)
   //    \end{eqnarray}
   //   </latex>
   //   
-  //   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:
@@ -70,6 +70,7 @@ function [xopt,fopt,exitflag,output,lambda] = qpipopt (varargin)
   //      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
   //    
   // Examples 
   //    //Find the value of x that minimize following function
@@ -89,7 +90,6 @@ function [xopt,fopt,exitflag,output,lambda] = qpipopt (varargin)
   //	nbVar = 2;
   //	nbCon = 3;
   //	[xopt,fopt,exitflag,output,lambda] = qpipopt(nbVar,nbCon,Q,p,lb,ub,conMatrix,conLB,conUB)
-  // 
   // Authors
   // Keyur Joshi, Saikiran, Iswarya, Harpreet Singh
     
diff --git a/macros/qpipoptmat.bin b/macros/qpipoptmat.bin
index 7a37d9a..ad893f2 100644
--- a/macros/qpipoptmat.bin
+++ b/macros/qpipoptmat.bin
diff --git a/macros/qpipoptmat.sci b/macros/qpipoptmat.sci
index 3f58e70..eec93ce 100644
--- a/macros/qpipoptmat.sci
+++ b/macros/qpipoptmat.sci
@@ -11,87 +11,85 @@
 
 
 function [xopt,fopt,exitflag,output,lambda] = qpipoptmat (varargin)
-  // Solves a linear quadratic problem.
-  //
-  //   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,fopt,exitflag,output,lamda] = qpipoptmat( ... )
-  //   
-  //   Parameters
-  //   H : a symmetric matrix of doubles, represents coefficients of quadratic in the quadratic problem.
-  //   f : a vector of doubles, represents coefficients of linear in the quadratic problem
-  //   A : a vector of doubles, represents the linear coefficients in the inequality constraints
-  //   b : a vector of doubles, represents the linear coefficients in the inequality constraints
-  //   Aeq : a 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 vector of doubles, contains lower bounds of the variables.
-  //   UB : a vector of doubles, contains upper bounds of the variables.
-  //   x0 : a vector of doubles, contains initial guess of variables.
-  //   param : a list containing the the parameters to be set.
-  //   xopt : a vector of doubles, the computed solution of the optimization problem.
-  //   fopt : a double, 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 
-  //
-  //   <latex>
-  //    \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}
-  //   </latex>
-  //   
-  //   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
-  //      //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];
-  //      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];
-  //      x0 = repmat(0,6,1);
-  //	  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)
-  //
-  // Authors
-  // Keyur Joshi, Saikiran, Iswarya, Harpreet Singh
+	// Solves a linear quadratic problem.
+	//
+	//   Calling Sequence
+	//   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
+	//   H : a symmetric matrix of doubles, represents coefficients of quadratic in the quadratic problem.
+	//   f : a vector of doubles, represents coefficients of linear in the quadratic problem
+	//   A : a vector of doubles, represents the linear coefficients in the inequality constraints
+	//   b : a vector of doubles, represents the linear coefficients in the inequality constraints
+	//   Aeq : a 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 vector of doubles, contains lower bounds of the variables.
+	//   UB : a vector of doubles, contains upper bounds of the variables.
+	//   x0 : a vector of doubles, contains initial guess of variables.
+	//   param : a list containing the the parameters to be set.
+	//   xopt : a vector of doubles, the computed solution of the optimization problem.
+	//   fopt : a double, the function value at x.
+	//   exitflag : Integer identifying the reason the algorithm terminated.
+	//   output : 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).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 
+	//
+	//   <latex>
+	//    \begin{eqnarray}
+	//    &\mbox{min}_{x}
+	//    & 1/2*x'*H*x + f'*x  \\
+	//    & \text{subject to} & A*x \leq b \\
+	//    & & Aeq*x = beq \\
+	//    & & lb \leq x \leq ub \\
+	//    \end{eqnarray}
+	//   </latex>
+	//   
+	//   We are calling 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];
+	//	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];
+	//      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];
+	//      x0 = repmat(0,6,1);
+	//	  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)
+	// Authors
+	// Keyur Joshi, Saikiran, Iswarya, Harpreet Singh
     
     
 //To check the number of input and output argument
diff --git a/macros/symphony.bin b/macros/symphony.bin
index 3dab926..4bca695 100644
--- a/macros/symphony.bin
+++ b/macros/symphony.bin
diff --git a/macros/symphony.sci b/macros/symphony.sci
index eba9e64..b1a6f28 100644
--- a/macros/symphony.sci
+++ b/macros/symphony.sci
@@ -10,155 +10,156 @@
 // http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt
 
 function [xopt,fopt,status,output] = symphony (varargin)
-  // Solves a  mixed integer linear programming constrained optimization problem.
-  //
-  //   Calling Sequence
-  //   xopt = symphony(nbVar,nbCon,objCoef,isInt,LB,UB,conMatrix,conLB,conUB)
-  //   xopt = symphony(nbVar,nbCon,objCoef,isInt,LB,UB,conMatrix,conLB,conUB,objSense)
-  //   xopt = symphony(nbVar,nbCon,objCoef,isInt,LB,UB,conMatrix,conLB,conUB,objSense,options)
-  //   [xopt,fopt,status,output] = symphony( ... )
-  //   
-  //   Parameters
-  //   nbVar : a double, number of variables.
-  //   nbCon : a double, number of constraints.
-  //   objCoeff : a vector of doubles, represents coefficients of the variables in the objective.
-  //   isInt : a vector of boolean, represents wether a variable is constrained to be an integer.
-  //   LB : a vector of doubles, represents lower bounds of the variables.
-  //   UB : a vector of doubles, represents upper bounds of the variables.
-  //   conMatrix : a matrix of doubles, represents  matrix representing the constraint matrix.
-  //   conLB : a vector of doubles, represents lower bounds of the constraints. 
-  //   conUB : a vector of doubles, represents upper bounds of the constraints
-  //   objSense : The sense (maximization/minimization) of the objective. Use 1(sym_minimize ) or -1 (sym_maximize) here.
-  //   options : a a list containing the the parameters to be set.
-  //   xopt : a vector of doubles, the computed solution of the optimization problem.
-  //   fopt : a double, the function value at x.
-  //   status : status flag from symphony.
-  //   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 
-  //
-  //   <latex>
-  //    \begin{eqnarray}
-  //    &\mbox{min}_{x}
-  //    & f(x) \\
-  //    & \text{subject to} & conLB \leq C(x) \leq conUB \\
-  //    & & lb \leq x \leq ub \\
-  //    \end{eqnarray}
-  //   </latex>
-  //   
-  //   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
-  //    //A basic case : 
-  //    // Objective function
-  //    c = [350*5,330*3,310*4,280*6,500,450,400,100]';
-  //    // Lower Bound of variable
-  //    lb = repmat(0,8,1);
-  //    // Upper Bound of variables
-  //    ub = [repmat(1,4,1);repmat(%inf,4,1)];
-  //    // Constraint Matrix
-  //    conMatrix = [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;]
-  //    // Lower Bound of constrains
-  //    conlb = [ 25; 1.25; 1.25]
-  //    // Upper Bound of constrains
-  //    conub = [ 25; 1.25; 1.25]
-  //    // Row Matrix for telling symphony that the is integer or not
-  //    isInt = [repmat(%t,1,4) repmat(%f,1,4)];
-  //    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)
-  //
-    //    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)
-    //    p = [   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
-    //    conMatrix = [ 
-    //                    //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 ;
-    //     ];
-    //    nbCon = size(conMatrix,1)
-    //    nbVar = size(conMatrix,2)
-    //    // Lower Bound of variables
-    //    lb = repmat(0,nbVar,1)
-    //    // Upper Bound of variables
-    //    ub = repmat(1,nbVar,1)
-    //    // Row Matrix for telling symphony that the is integer or not
-    //    isInt = repmat(%t,1,nbVar)
-    //    // Lower Bound of constrains
-    //    conLB=repmat(0,nbCon,1);
-    //    // Upper Bound of constraints
-    //    conUB=[11927 13727 11551 13056 13460 ]';
-    //    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] = symphony(nbVar,nbCon,p,isInt,lb,ub,conMatrix,conLB,conUB,-1,options)
-    //
-    // Authors
-    // Keyur Joshi, Saikiran, Iswarya, Harpreet Singh
+	// Solves a  mixed integer linear programming constrained optimization problem.
+	//
+	//   Calling Sequence
+	//   xopt = symphony(nbVar,nbCon,objCoef,isInt,LB,UB,conMatrix,conLB,conUB)
+	//   xopt = symphony(nbVar,nbCon,objCoef,isInt,LB,UB,conMatrix,conLB,conUB,objSense)
+	//   xopt = symphony(nbVar,nbCon,objCoef,isInt,LB,UB,conMatrix,conLB,conUB,objSense,options)
+	//   [xopt,fopt,status,output] = symphony( ... )
+	//   
+	//   Parameters
+	//   nbVar : a double, number of variables.
+	//   nbCon : a double, number of constraints.
+	//   objCoeff : a vector of doubles, represents coefficients of the variables in the objective.
+	//   isInt : a vector of boolean, represents wether a variable is constrained to be an integer.
+	//   LB : a vector of doubles, represents lower bounds of the variables.
+	//   UB : a vector of doubles, represents upper bounds of the variables.
+	//   conMatrix : a matrix of doubles, represents  matrix representing the constraint matrix.
+	//   conLB : a vector of doubles, represents lower bounds of the constraints. 
+	//   conUB : a vector of doubles, represents upper bounds of the constraints
+	//   objSense : The sense (maximization/minimization) of the objective. Use 1(sym_minimize ) or -1 (sym_maximize) here.
+	//   options : a a list containing the the parameters to be set.
+	//   xopt : a vector of doubles, the computed solution of the optimization problem.
+	//   fopt : a double, the function value at x.
+	//   status : status flag from symphony.
+	//   output : 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 
+	//
+	//   <latex>
+	//    \begin{eqnarray}
+	//    &\mbox{min}_{x}
+	//    & 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}
+	//   </latex>
+	//   
+	//   We are calling SYMPHONY written in C by gateway files for the actual computation.
+	//
+	//   Examples
+	//    //A basic case : 
+	//    // Objective function
+	//    c = [350*5,330*3,310*4,280*6,500,450,400,100]';
+	//    // Lower Bound of variable
+	//    lb = repmat(0,8,1);
+	//    // Upper Bound of variables
+	//    ub = [repmat(1,4,1);repmat(%inf,4,1)];
+	//    // Constraint Matrix
+	//    conMatrix = [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;]
+	//    // Lower Bound of constrains
+	//    conlb = [ 25; 1.25; 1.25]
+	//    // Upper Bound of constrains
+	//    conub = [ 25; 1.25; 1.25]
+	//    // Row Matrix for telling symphony that the is integer or not
+	//    isInt = [repmat(%t,1,4) repmat(%f,1,4)];
+	//    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 
+	//
+	//    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)
+	//    p = [   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
+	//    conMatrix = [ 
+	//                    //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 ;
+	//     ];
+	//    nbCon = size(conMatrix,1)
+	//    nbVar = size(conMatrix,2)
+	//    // Lower Bound of variables
+	//    lb = repmat(0,nbVar,1)
+	//    // Upper Bound of variables
+	//    ub = repmat(1,nbVar,1)
+	//    // Row Matrix for telling symphony that the is integer or not
+	//    isInt = repmat(%t,1,nbVar)
+	//    // Lower Bound of constrains
+	//    conLB=repmat(0,nbCon,1);
+	//    // Upper Bound of constraints
+	//    conUB=[11927 13727 11551 13056 13460 ]';
+	//    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] = symphony(nbVar,nbCon,p,isInt,lb,ub,conMatrix,conLB,conUB,-1,options);
+	// Authors
+	// Keyur Joshi, Saikiran, Iswarya, Harpreet Singh
     
 //To check the number of input and output argument
    [lhs , rhs] = argn();
diff --git a/macros/symphonymat.bin b/macros/symphonymat.bin
index 8d42926..08b1616 100644
--- a/macros/symphonymat.bin
+++ b/macros/symphonymat.bin
diff --git a/macros/symphonymat.sci b/macros/symphonymat.sci
index 5aab6e5..40b07eb 100644
--- a/macros/symphonymat.sci
+++ b/macros/symphonymat.sci
@@ -32,7 +32,7 @@ function [xopt,fopt,status,iter] = symphonymat (varargin)
   //   xopt : a vector of double, the computed solution of the optimization problem
   //   fopt : a doubles, the function value at x
   //   status : status flag from symphony.
-  //   output : The output data structure contains detailed informations about the optimization process.
+  //   output : 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 :
@@ -41,14 +41,15 @@ function [xopt,fopt,status,iter] = symphonymat (varargin)
   //   <latex>
   //    \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}
   //   </latex>
   //   
-  //   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
   //    // Objective function
@@ -65,6 +66,7 @@ function [xopt,fopt,status,iter] = symphonymat (varargin)
   //    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
@@ -147,7 +149,6 @@ function [xopt,fopt,status,iter] = symphonymat (varargin)
   //    fopt = [ 24381 ]
   //    // Calling Symphony
   //    [x,f,status,output] = symphonymat(objCoef,intcon,conMatrix,conUB,[],[],lb,ub,options);
-  // 
   // Authors
   // Keyur Joshi, Saikiran, Iswarya, Harpreet Singh