Bugs fixed 3

1 files changed, 18 insertions, 18 deletions

diff --git a/macros/symphonymat.sci b/macros/symphonymat.sci
index 40b07eb..f7e08ac 100644
--- a/macros/symphonymat.sci
+++ b/macros/symphonymat.sci
@@ -13,24 +13,24 @@ function [xopt,fopt,status,iter] = symphonymat (varargin)
   // Solves a mixed integer linear programming constrained optimization problem in intlinprog format.
   //
   //   Calling Sequence
-  //   xopt = symphonymat(f,intcon,A,b)
-  //   xopt = symphonymat(f,intcon,A,b,Aeq,beq)
-  //   xopt = symphonymat(f,intcon,A,b,Aeq,beq,lb,ub)
-  //   xopt = symphonymat(f,intcon,A,b,Aeq,beq,lb,ub,options)
+  //   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
-  //   f : a vector of doubles, contains coefficients of the variables in the objective 
+  //   f : 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 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.
+  //   A : Linear inequality constraint matrix, specified as a matrix of double. 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 double. 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 double. 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 double. 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 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 doubles, the function value at x
+  //   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.
   //   
@@ -41,7 +41,7 @@ function [xopt,fopt,status,iter] = symphonymat (varargin)
   //   <latex>
   //    \begin{eqnarray}
   //    &\mbox{min}_{x}
-  //    & f^T*x \\
+  //    & C^T*x \\
   //    & \text{subject to} & A*x \leq b \\
   //    & & Aeq*x = beq \\
   //    & & lb \leq x \leq ub \\
@@ -53,7 +53,7 @@ function [xopt,fopt,status,iter] = symphonymat (varargin)
   //
   // Examples
   //    // Objective function
-  //    c = [350*5,330*3,310*4,280*6,500,450,400,100]';
+  //    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
@@ -79,7 +79,7 @@ function [xopt,fopt,status,iter] = symphonymat (varargin)
   //    // 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)
-  //    objCoef = -1*[   504 803 667 1103 834 585 811 856 690 832 846 813 868 793 ..
+  //    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 ..
@@ -87,7 +87,7 @@ function [xopt,fopt,status,iter] = symphonymat (varargin)
   //            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
+  //    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 ..
@@ -129,7 +129,7 @@ function [xopt,fopt,status,iter] = symphonymat (varargin)
   //                    893 160 785 311 417 748 375 362 617 553 474 915 457 261 350 635 ;
   //     ];
   //    nbVar = size(objCoef,1)
-  //    conUB=[11927 13727 11551 13056 13460 ];
+  //    b=[11927 13727 11551 13056 13460 ];
   //    // Lower Bound of variables
   //    lb = repmat(0,1,nbVar)
   //    // Upper Bound of variables
@@ -148,7 +148,7 @@ function [xopt,fopt,status,iter] = symphonymat (varargin)
   //    // Optimal value
   //    fopt = [ 24381 ]
   //    // Calling Symphony
-  //    [x,f,status,output] = symphonymat(objCoef,intcon,conMatrix,conUB,[],[],lb,ub,options);
+  //    [x,f,status,output] = symphonymat(C,intcon,A,b,[],[],lb,ub,options);
   // Authors
   // Keyur Joshi, Saikiran, Iswarya, Harpreet Singh