Back Up Files Deleted

22 files changed, 0 insertions, 2669 deletions

diff --git a/demos/qpipoptmat.dem.sce~ b/demos/qpipoptmat.dem.sce~
deleted file mode 100644
index 79628a7..0000000
--- a/demos/qpipoptmat.dem.sce~
+++ /dev/null
@@ -1,42 +0,0 @@
-mode(1)
-//
-// Demo of qpipoptmat.sci
-//
-
-//Find x in R^6 such that:
-halt()   // Press return to continue
- 
-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;
-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];
-b = [2; 2; 3];
-lb = [0; 0];
-ub = [%inf; %inf];
-[xopt,fopt,exitflag,output,lambda] = qpipoptmat(H,f,A,b,[],[],lb,ub)
-halt()   // Press return to continue
- 
-//========= E N D === O F === D E M O =========//
diff --git a/demos/sci_symphony.dem.gateway.sce~ b/demos/sci_symphony.dem.gateway.sce~
deleted file mode 100644
index 9256ca2..0000000
--- a/demos/sci_symphony.dem.gateway.sce~
+++ /dev/null
@@ -1,16 +0,0 @@
-// 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
-
-demopath = get_absolute_file_path("sci_symphony.dem.gateway.sce");
-
-subdemolist = ["Symphony for knapsack", "symphony_knapsack.sce"];
-
-subdemolist(:,2) = demopath + subdemolist(:,2);
diff --git a/demos/symphony_knapsack.sce b/demos/symphony_knapsack.sce
deleted file mode 100644
index 42c192c..0000000
--- a/demos/symphony_knapsack.sce
+++ /dev/null
@@ -1,116 +0,0 @@
-mode (-1)
-
-// Reference
-// 
-// 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(0,1,nbVar)
-
-// Upper Bound of variables
-ub = repmat(1,1,nbVar)
-
-// 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 = ["tie_limit" "40"];
-
-// 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)
-
- 
-//========= E N D === O F === D E M O =========//
-//
-// Load this script into the editor
-//
-filename = "symphony_knapsack.sce";
-dname = get_absolute_file_path(filename);
-editor ( dname + filename );
diff --git a/demos/symphony_mat_knapsack.sce b/demos/symphony_mat_knapsack.sce
deleted file mode 100644
index 47c85e2..0000000
--- a/demos/symphony_mat_knapsack.sce
+++ /dev/null
@@ -1,90 +0,0 @@
-mode (-1)
-
-// Reference
-// 
-// 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)
-objCoef = -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
-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 ;
- ];
-nbVar = size(objCoef,2)
-conUB=[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 = ["time_limit" "40"];
-
-// 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,iter] = symphony_mat(objCoef,intcon,conMatrix,conUB,[],[],lb,ub,options);
-
diff --git a/demos/symphonymat.dem.sce~ b/demos/symphonymat.dem.sce~
deleted file mode 100644
index ef4d7cc..0000000
--- a/demos/symphonymat.dem.sce~
+++ /dev/null
@@ -1,104 +0,0 @@
-mode(1)
-//
-// Demo of symphonymat.sci
-//
-
-// 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)
-halt()   // Press return to continue
- 
-// 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)
-objCoef = -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
-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 ;
-];
-nbVar = size(objCoef,2)
-conUB=[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(objCoef,intcon,conMatrix,conUB,[],[],lb,ub);
-halt()   // Press return to continue
- 
-//========= E N D === O F === D E M O =========//
diff --git a/etc/README.rst~ b/etc/README.rst~
deleted file mode 100644
index e69de29..0000000
--- a/etc/README.rst~
+++ /dev/null
diff --git a/macros/README.rst~ b/macros/README.rst~
deleted file mode 100644
index 5a07f63..0000000
--- a/macros/README.rst~
+++ /dev/null
@@ -1,36 +0,0 @@
-MACROS
-======
-
-These files mainly consist of functions for checking the input and calling the gateway functions
-
-symphony
---------
-
-It takes the input in symphony style and checks the input. After all the checks call the symphony_call function.
-
-symphonymat
------------
-
-It takes the input in symphony style and checks the input. After all the checks call the symphony_call function.
-
-symphony_call
--------------
-
-It calls the gateway functions to initialize, set options and to solve it. After that it will call the functions to get the solution for the problem.
-
-setOptions
-----------
-
-It will set the options in the symphony.
-
-qpipopt
--------
-
-It synatize the input and call solveqp in the ipopt style.
-
-qpipopt
--------
-
-It synatize the input and call solveqp in the quadprog style.
-
-
diff --git a/macros/qpipopt.sci~ b/macros/qpipopt.sci~
deleted file mode 100644
index 35e604b..0000000
--- a/macros/qpipopt.sci~
+++ /dev/null
@@ -1,233 +0,0 @@
-// 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
-
-
-function [xopt,fopt,exitflag,output,lambda] = qpipopt (varargin)
-  // Solves a linear quadratic problem.
-  //
-  //   Calling Sequence
-  //   xopt = qpipopt(nbVar,nbCon,Q,p,LB,UB,conMatrix,conLB,conUB)
-  //   xopt = qpipopt(nbVar,nbCon,Q,p,LB,UB,conMatrix,conLB,conUB,x0)
-  //   xopt = qpipopt(nbVar,nbCon,Q,p,LB,UB,conMatrix,conLB,conUB,x0,param)
-  //   [xopt,fopt,exitflag,output,lamda] = qpipopt( ... )
-  //   
-  //   Parameters
-  //   nbVar : a 1 x 1 matrix of doubles, number of variables
-  //   nbCon : a 1 x 1 matrix of doubles, number of constraints
-  //   Q : a n x n symmetric matrix of doubles, where n is number of variables, represents coefficients of quadratic in the quadratic problem.
-  //   p : a n x 1 matrix of doubles, where n is number of variables, represents coefficients of linear in the quadratic problem
-  //   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.
-  //   conMatrix : a m x n matrix of doubles, where n is number of variables and m is number of constraints, contains  matrix representing the constraint matrix 
-  //   conLB : a m x 1 matrix of doubles, where m is number of constraints, contains lower bounds of the constraints. 
-  //   conUB : a m x 1 matrix of doubles, where m is number of constraints, contains upper bounds of the constraints. 
-  //   x0 : a m x 1 matrix of doubles, where m is number of constraints, contains initial guess of variables.
-  //   param : a list containing the the parameters to be set.
-  //   xopt : a 1xn 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 
-  //
-  //   <latex>
-  //    \begin{eqnarray}
-  //    &\mbox{min}_{x}
-  //    & 1/2*x'*Q*x + p'*x  \\
-  //    & \text{subject to} & conLB \leq C(x) \leq conUB \\
-  //    & & 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:
-  //      conMatrix= [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'*Q*x + p'*x with
-  //      p=[1; 2; 3; 4; 5; 6]; Q=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,Q,p,lb,ub,conMatrix,conLB,conUB,x0,param)
-  //    
-  // 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.
-  //	Q = [1 -1; -1 2]; 
-  //	p = [-2; -6];
-  //    conMatrix = [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,Q,p,lb,ub,conMatrix,conLB,conUB)
-  // 
-  // Authors
-  // Keyur Joshi, Saikiran, Iswarya, Harpreet Singh
-    
-    
-//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
-   
-   
-   nbVar = varargin(1);
-   nbCon = varargin(2);
-   Q = varargin(3);
-   p = varargin(4);
-   LB = varargin(5);
-   UB = varargin(6);
-   conMatrix = varargin(7);
-   conLB = varargin(8);
-   conUB = varargin(9);
-
-   
-   if ( rhs<10 | size(varargin(10)) ==0 ) then
-      x0 = repmat(0,nbVar,1);
-   else
-      x0 = varargin(10);
-  end
-   
-   if ( rhs<11 ) then
-      param = []; 
-   else
-      param =varargin(11);
-   end
-   
-   if (modulo(size(param),2)) then
-	errmsg = msprintf(gettext("%s: Size of parameters should be even"), "qpipopt");
-	error(errmsg);
-   end
-
-
-   options = list(..
-      "MaxIter"     , [3000], ...
-      "CpuTime"   , [600] ...
-      );
-
-   for i = 1:(size(param))/2
-       
-       	select param(2*i-1)
-    	case "MaxIter" then
-          		options(1) = param(2*i);
-       	case "CpuTime" then
-          		options(3) = param(2*i);
-    	else
-    	      errmsg = msprintf(gettext("%s: Unrecognized parameter name ''%s''."), "qpipopt", param(2*i-1));
-    	      error(errmsg)
-    	end
-   end
-
-   //IPOpt wants it in row matrix form
-   p = p';
-   LB = LB';
-   UB = UB';
-   conLB = conLB';
-   conUB = conUB';
-   x0 = x0';
-   
-   //Checking the Q matrix which needs to be a symmetric matrix
-   if ( ~isequal(Q,Q') ) then
-      errmsg = msprintf(gettext("%s: Q is not a symmetric matrix"), "qpipopt");
-      error(errmsg);
-   end
-
-   //Check the size of Q which should equal to the number of variable
-   if ( size(Q) ~= [nbVar nbVar]) then
-      errmsg = msprintf(gettext("%s: The Size of Q is not equal to the number of variables"), "qpipopt");
-      error(errmsg);
-   end
-   
-   //Check the size of p which should equal to the number of variable
-   if ( size(p,2) ~= [nbVar]) then
-      errmsg = msprintf(gettext("%s: The Size of p is not equal to the number of variables"), "qpipopt");
-      error(errmsg);
-   end
-   
-   
-   //Check the size of constraint which should equal to the number of variables
-   if ( size(conMatrix,2) ~= nbVar) then
-      errmsg = msprintf(gettext("%s: The size of constraints is not equal to the number of variables"), "qpipopt");
-      error(errmsg);
-   end
-
-   //Check the size of Lower Bound which should equal to the number of variables
-   if ( size(LB,2) ~= nbVar) then
-      errmsg = msprintf(gettext("%s: The Lower Bound is not equal to the number of variables"), "qpipopt");
-      error(errmsg);
-   end
-
-   //Check the size of Upper Bound which should equal to the number of variables
-   if ( size(UB,2) ~= nbVar) then
-      errmsg = msprintf(gettext("%s: The Upper Bound is not equal to the number of variables"), "qpipopt");
-      error(errmsg);
-   end
-
-   //Check the size of constraints of Lower Bound which should equal to the number of constraints
-   if ( size(conLB,2) ~= nbCon) then
-      errmsg = msprintf(gettext("%s: The Lower Bound of constraints is not equal to the number of constraints"), "qpipopt");
-      error(errmsg);
-   end
-
-   //Check the size of constraints of Upper Bound which should equal to the number of constraints
-   if ( size(conUB,2) ~= nbCon) then
-      errmsg = msprintf(gettext("%s: The Upper Bound of constraints is not equal to the number of constraints"), "qpipopt");
-      error(errmsg);
-   end
-    
-   //Check the size of initial of variables which should equal to the number of variables
-   if ( size(x0,2) ~= nbVar) then
-      errmsg = msprintf(gettext("%s: The initial guess of variables is not equal to the number of variables"), "qpipopt");
-      error(errmsg);
-   end
-    
-   
-   [xopt,fopt,status,iter,Zl,Zu,lmbda] = solveqp(nbVar,nbCon,Q,p,conMatrix,conLB,conUB,LB,UB,x0,options);
-   
-   xopt = xopt';
-   exitflag = status;
-   output = struct("Iterations"      , []);
-   output.Iterations = iter;
-   lambda = struct("lower"           , [], ..
-                   "upper"           , [], ..
-                   "constraint"      , []);
-   
-   lambda.lower = Zl;
-   lambda.upper = Zu;
-   lambda.constraint = lmbda;
-    
-
-endfunction
diff --git a/macros/qpipoptmat.sci~ b/macros/qpipoptmat.sci~
deleted file mode 100644
index e29da8f..0000000
--- a/macros/qpipoptmat.sci~
+++ /dev/null
@@ -1,265 +0,0 @@
-// 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
-
-
-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 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.
-  //   x0 : a m x 1 matrix of doubles, where m is number of constraints, contains initial guess of variables.
-  //   param : a list containing the the parameters to be set.
-  //   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 
-  //
-  //   <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
-    
-    
-//To check the number of input and output argument
-   [lhs , rhs] = argn();
-	
-//To check the number of argument given by user
-   if ( rhs < 2 | rhs == 3 | rhs == 5 | rhs == 7 | rhs > 10 ) then
-    errmsg = msprintf(gettext("%s: Unexpected number of input arguments : %d provided while should be in the set of [2 4 6 8 9 10]"), "qpipoptmat", rhs);
-    error(errmsg)
-   end
-   
-   H = varargin(1);
-   f = varargin(2);
-   nbVar = size(H,1);
-
-   
-   if ( rhs<2 ) then
-      A = []
-      b = []
-   else
-      A = varargin(3);
-      b = varargin(4);
-  end
-      
-   if ( rhs<4 ) then
-      Aeq = []
-      beq = []
-   else
-      Aeq = varargin(5);
-      beq = varargin(6);
-  end
-  
-  if ( rhs<6 ) then
-      LB = repmat(-%inf,nbVar,1);
-      UB = repmat(%inf,nbVar,1);
-   else
-      LB = varargin(7);
-      UB = varargin(8);
-  end
-
-
-  if ( rhs<10 | size(varargin(9)) ==0 ) then
-      x0 = repmat(0,nbVar,1)
-  else
-      x0 = varargin(9);
-  end
-
-   if ( rhs<11 ) then
-      param = list();
-   else
-      param =varargin(10);
-   end
-   
-
-   if (modulo(size(param),2)) then
-	errmsg = msprintf(gettext("%s: Size of parameters should be even"), "qpipoptmat");
-	error(errmsg);
-   end
-
-
-   options = list(..
-      "MaxIter"     , [3000], ...
-      "CpuTime"   , [600] ...
-      );
-
-   for i = 1:(size(param))/2
-        
-       	select param(2*i-1)
-    	case "MaxIter" then
-          		options(2*i-1) = param(2*i);
-       	case "CpuTime" then
-          		options(2*i-1) = param(2*i);
-    	else
-    	      errmsg = msprintf(gettext("%s: Unrecognized parameter name ''%s''."), "qpipoptmat", param(2*i-1));
-    	      error(errmsg)
-    	end
-   end
-
-   nbConInEq = size(A,1);
-   nbConEq = size(Aeq,1);
-
-   //Checking the H matrix which needs to be a symmetric matrix
-   if ( H~=H') then
-      errmsg = msprintf(gettext("%s: H is not a symmetric matrix"), "qpipoptmat");
-      error(errmsg);
-   end
-
-   //Check the size of H which should equal to the number of variable
-   if ( size(H) ~= [nbVar nbVar]) then
-      errmsg = msprintf(gettext("%s: The Size of H is not equal to the number of variables"), "qpipoptmat");
-      error(errmsg);
-   end
-   
-   //Check the size of f which should equal to the number of variable
-   if ( size(f,1) ~= [nbVar]) then
-      errmsg = msprintf(gettext("%s: The Size of f is not equal to the number of variables"), "qpipoptmat");
-      error(errmsg);
-   end
-   
-   
-   //Check the size of inequality constraint which should be equal to the number of variables
-   if ( size(A,2) ~= nbVar & size(A,2) ~= 0) then
-      errmsg = msprintf(gettext("%s: The size of inequality constraints is not equal to the number of variables"), "qpipoptmat");
-      error(errmsg);
-   end
-
-   //Check the size of equality constraint which should be equal to the number of variables
-   if ( size(Aeq,2) ~= nbVar & size(Aeq,2) ~= 0 ) then
-      errmsg = msprintf(gettext("%s: The size of equality constraints is not equal to the number of variables"), "qpipoptmat");
-      error(errmsg);
-   end
-
-
-   //Check the size of Lower Bound which should be equal to the number of variables
-   if ( size(LB,1) ~= nbVar) then
-      errmsg = msprintf(gettext("%s: The Lower Bound is not equal to the number of variables"), "qpipoptmat");
-      error(errmsg);
-   end
-
-   //Check the size of Upper Bound which should equal to the number of variables
-   if ( size(UB,1) ~= nbVar) then
-      errmsg = msprintf(gettext("%s: The Upper Bound is not equal to the number of variables"), "qpipoptmat");
-      error(errmsg);
-   end
-
-   //Check the size of constraints of Lower Bound which should equal to the number of constraints
-   if ( size(b,1) ~= nbConInEq & size(b,1) ~= 0) then
-      errmsg = msprintf(gettext("%s: The Lower Bound of inequality constraints is not equal to the number of constraints"), "qpipoptmat");
-      error(errmsg);
-   end
-
-   //Check the size of constraints of Upper Bound which should equal to the number of constraints
-   if ( size(beq,1) ~= nbConEq & size(beq,1) ~= 0) then
-      errmsg = msprintf(gettext("%s: The Upper Bound of equality constraints is not equal to the number of constraints"), "qpipoptmat");
-      error(errmsg);
-   end
-
-   //Check the size of initial of variables which should equal to the number of variables
-   if ( size(x0,1) ~= nbVar) then
-      errmsg = msprintf(gettext("%s: The initial guess of variables is not equal to the number of variables"), "qpipoptmat");
-      error(errmsg);
-   end
-
-   
-   //Converting it into ipopt format
-   f = f';
-   LB = LB';
-   UB = UB';
-   x0 = x0';
-   conMatrix = [Aeq;A];
-   nbCon = size(conMatrix,1);
-   conLB = [beq; repmat(-%inf,nbConInEq,1)]';
-   conUB = [beq;b]' ; 
-   [xopt,fopt,status,iter,Zl,Zu,lmbda] = solveqp(nbVar,nbCon,H,f,conMatrix,conLB,conUB,LB,UB,x0,options);
-   
-   xopt = xopt';
-   exitflag = status;
-   output = struct("Iterations"      , []);
-   output.Iterations = iter;
-   lambda = struct("lower"           , [], ..
-                   "upper"           , [], ..
-                   "ineqlin"         , [], ..
-                   "eqlin"           , []);
-   
-   lambda.lower = Zl;
-   lambda.upper = Zu;
-   lambda.eqlin = lmbda(1:nbConEq);
-   lambda.ineqlin = lmbda(nbConEq+1:nbCon);
-    
-
-endfunction
diff --git a/macros/setOptions.sci~ b/macros/setOptions.sci~
deleted file mode 100644
index ef5c36c..0000000
--- a/macros/setOptions.sci~
+++ /dev/null
@@ -1,40 +0,0 @@
-// 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
-
-function setOptions(varargin)
-
-    options = varargin(1);
-    nbOpt = size(options);
-    
-
-    if (nbOpt~=0) then
-        for i = 1:(nbOpt/2)
-        
-        //Setting the parameters
-          
-            //Check if the given parameter is String
-            if (type(options(2*i)) == 10 ) then
-                sym_setStrParam(options(2*i - 1),options(2*i));
-          
-            //Check if the given parameter is Double
-            elseif(type(options(2*i))==1) then
-                sym_setDblParam(options(2*i - 1),options(2*i));
-             
-            //Check if the given parameter is Integer
-            elseif(type(options(2*i))==8)
-                sym_setIntParam(options(2*i - 1),options(2*i)); 
-            end
-          
-        end
-    end
-    
-endfunction
-
diff --git a/macros/symphony.sci~ b/macros/symphony.sci~
deleted file mode 100644
index 4b11ae8..0000000
--- a/macros/symphony.sci~
+++ /dev/null
@@ -1,287 +0,0 @@
-// 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
-
-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 1 x n matrix of doubles, where n is number of variables, 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
-    
-//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 in the set [9 10 11]"), "Symphony", rhs);
-    error(errmsg)
-   end
-   
-   nbVar = varargin(1);
-   nbCon = varargin(2);
-   objCoef = varargin(3);
-   isInt = varargin(4);
-   LB = varargin(5);
-   UB = varargin(6);
-   conMatrix = varargin(7);
-   conLB = varargin(8);
-   conUB = varargin(9);
-   
-   if ( rhs<10 ) then
-      objSense = 1;
-   else
-      objSense = varargin(10);
-   end
-   
-   if (rhs<11|size(varargin(11))==0) then
-      options = list();
-   else
-      options = varargin(11);
-   end
-
-// Check if the user gives row vector 
-// and Changing it to a column matrix
-
-   if (size(isInt,2)== [nbVar]) then
-	isInt = isInt';
-   end
-
-   if (size(LB,2)== [nbVar]) then
-	LB = LB';
-   end
-
-   if (size(UB,2)== [nbVar]) then
-	UB = UB';
-   end
-
-   if (size(conLB,2)== [nbVar]) then
-	conLB = conLB';
-   end
-
-   if (size(conUB,2)== [nbVar]) then
-	conUB = conUB';
-   end
-
-
-   if (size(objCoef,2)~=1) then
-	errmsg = msprintf(gettext("%s: Objective Coefficients should be a column matrix"), "Symphony");
-	error(errmsg);
-   end
-
-   if (size(objCoef,1)~=nbVar) then
-	errmsg = msprintf(gettext("%s: Number of variables in Objective Coefficients is not equal to number of variables given"), "Symphony");
-	error(errmsg);
-   end
-
-   //Check the size of isInt which should equal to the number of variables
-   if(size(isInt,1)~=nbVar) then
-	errmsg = msprintf(gettext("%s: The size of isInt is not equal to the number of variables"), "Symphony");
-	error(errmsg);
-   end
-
-   //Check the size of lower bound of inequality constraint which should equal to the number of constraints
-   if ( size(conLB,1) ~= nbCon) then
-      errmsg = msprintf(gettext("%s: The Lower Bound of constraint is not equal to the number of constraint"), "Symphony");
-      error(errmsg);
-   end
-
-   //Check the size of lower bound of inequality constraint which should equal to the number of constraints
-   if ( size(conUB,1) ~= nbCon) then
-      errmsg = msprintf(gettext("%s: The Upper Bound of constraint is not equal to the number of constraint"), "Symphony");
-      error(errmsg);
-   end
-
-   //Check the row of constraint which should equal to the number of constraints
-   if ( size(conMatrix,1) ~= nbCon) then
-      errmsg = msprintf(gettext("%s: The number of rows in constraint should be equal to the number of constraints"), "Symphony");
-      error(errmsg);
-   end
-
-   //Check the column of constraint which should equal to the number of variables
-   if ( size(conMatrix,2) ~= nbVar) then
-      errmsg = msprintf(gettext("%s: The number of columns in constraint should equal to the number of variables"), "Symphony");
-      error(errmsg);
-   end
-
-   //Check the size of Lower Bound which should equal to the number of variables
-   if ( size(LB,1) ~= nbVar) then
-      errmsg = msprintf(gettext("%s: The Lower Bound is not equal to the number of variables"), "Symphony");
-      error(errmsg);
-   end
-
-   //Check the size of Upper Bound which should equal to the number of variables
-   if ( size(UB,1) ~= nbVar) then
-      errmsg = msprintf(gettext("%s: The Upper Bound is not equal to the number of variables"), "Symphony");
-      error(errmsg);
-   end
-   
-   if (type(options) ~= 15) then
-      errmsg = msprintf(gettext("%s: Options should be a list "), "Symphony");
-      error(errmsg);
-   end
-   
-   if (modulo(size(options),2)) then
-	errmsg = msprintf(gettext("%s: Size of parameters should be even"), "Symphony");
-	error(errmsg);
-   end
-
-   LB = LB';
-   UB = UB';
-   isInt = isInt';
-   objCoef = objCoef';
-
-   [xopt,fopt,status,output] = symphony_call(nbVar,nbCon,objCoef,isInt,LB,UB,conMatrix,conLB,conUB,objSense,options);
-
-endfunction
diff --git a/macros/symphony_call.sci~ b/macros/symphony_call.sci~
deleted file mode 100644
index 057ba63..0000000
--- a/macros/symphony_call.sci~
+++ /dev/null
@@ -1,52 +0,0 @@
-// 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
-
-function [xopt,fopt,status,output] = symphony_call(nbVar,nbCon,objCoef,isInt,LB,UB,conMatrix,conLB,conUB,objSense,options)
-
-    //Opening Symphony environment 
-    sym_open();
-
-    //Setting Options for the Symphpony
-//    setOptions(options);
-    
-   //Choosing to launch basic or advanced version
-    if(~issparse(conMatrix)) then
-        sym_loadProblemBasic(nbVar,nbCon,LB,UB,objCoef,isInt,objSense,conMatrix,conLB,conUB);
-    else
-        // Changing to Constraint Matrix into sparse matrix
-        conMatrix_advanced=sparse(conMatrix);
-        sym_loadProblem(nbVar,nbCon,LB,UB,objCoef,isInt,objSense,conMatrix_advanced,conLB,conUB);
-    end
-
-    op = sym_solve();
-    disp(op);
-    
-    xopt = [];
-    fopt = [];
-    status = [];
-    output = [];
-    
-     if (~op) then
-            xopt = sym_getVarSoln();
-            // Symphony gives a row matrix converting it to column matrix
-            xopt = xopt';
-            
-            fopt = sym_getObjVal();
-    end
-    
-    status = sym_getStatus();
-    
-    output = struct("Iterations"      , []);
-      
-      output.Iterations = sym_getIterCount();
-
-
-endfunction
diff --git a/macros/symphonymat.sci~ b/macros/symphonymat.sci~
deleted file mode 100644
index 455dd67..0000000
--- a/macros/symphonymat.sci~
+++ /dev/null
@@ -1,242 +0,0 @@
-// 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
-
-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,fopt,status,output] = symphonymat( ... )
-  //   
-  //   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 list containing the the parameters to be set.
-  //   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 
-  //
-  //   <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
-  //    // 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)
-  //
-  // 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)
-  //    objCoef = -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
-  //    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 ;
-  //     ];
-  //    nbVar = size(objCoef,2)
-  //    conUB=[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(objCoef,intcon,conMatrix,conUB,[],[],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 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)
-   end
-   
-   
-   objCoef = varargin(1)
-   intcon = varargin(2)
-   A = varargin(3)
-   b = varargin(4)
-   
-   nbVar = size(objCoef,2);
-   nbCon = size(A,1);
-   
-   if ( rhs<4 ) then
-      Aeq = []
-      beq = []
-   else
-      Aeq = varargin(5);
-      beq = varargin(6);
-      
-      if (size(Aeq,1)~=0) then
-           //Check the size of equality constraint which should equal to the number of inequality constraints
-            if ( size(Aeq,2) ~= nbVar) then
-                errmsg = msprintf(gettext("%s: The size of equality constraint is not equal to the number of variables"), "Symphony");
-                error(errmsg);
-            end
-          
-          //Check the size of upper bound of inequality constraint which should equal to the number of constraints
-            if ( size(beq,2) ~= size(Aeq,1)) then
-                errmsg = msprintf(gettext("%s: The equality constraint upper bound is not equal to the number of equality constraint"), "Symphony");
-                error(errmsg);
-            end
-      end
-      
-   end
-   
-   if ( rhs<6 ) then
-      lb = repmat(-%inf,1,nbVar);
-      ub = repmat(%inf,1,nbVar);
-   else
-      lb = varargin(7);
-      ub = varargin(8);
-   end
-   
-   if (rhs<8) then
-      options = list();
-   else
-      options = varargin(9);
-   end
-   
-
-//Check the size of lower bound of inequality constraint which should equal to the number of constraints
-   if ( size(b,2) ~= size(A,1)) then
-      errmsg = msprintf(gettext("%s: The Lower Bound of inequality constraint is not equal to the number of constraint"), "Symphony");
-      error(errmsg);
-   end
-
-//Check the size of Lower Bound which should equal to the number of variables
-   if ( size(lb,2) ~= nbVar) then
-      errmsg = msprintf(gettext("%s: The Lower Bound is not equal to the number of variables"), "Symphony");
-      error(errmsg);
-   end
-
-//Check the size of Upper Bound which should equal to the number of variables
-   if ( size(ub,2) ~= nbVar) then
-      errmsg = msprintf(gettext("%s: The Upper Bound is not equal to the number of variables"), "Symphony");
-      error(errmsg);
-   end
-   
-    //Changing the inputs in symphony's format 
-    conMatrix = [A;Aeq]
-    nbCon = size(conMatrix,1);
-    conLB = [repmat(-%inf,1,size(A,1)), beq]';
-    conUB = [b,beq]' ; 
-    
-    isInt = repmat(%f,1,nbVar);
-    for i=1:size(intcon,2)
-        isInt(intcon(i)) = %t
-    end
-    
-    objSense = 1;
-    
-   [xopt,fopt,status,iter] = symphony_call(nbVar,nbCon,objCoef,isInt,lb,ub,conMatrix,conLB,conUB,objSense,options);
-
-endfunction
diff --git a/sci_gateway/cpp/QuadNLP.hpp~ b/sci_gateway/cpp/QuadNLP.hpp~
deleted file mode 100644
index 5e1047f..0000000
--- a/sci_gateway/cpp/QuadNLP.hpp~
+++ /dev/null
@@ -1,134 +0,0 @@
-/*
- * 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.
- *
-*/
-
-#ifndef __QuadNLP_HPP__
-#define __QuadNLP_HPP__
-
-#include "IpTNLP.hpp"
-extern "C"{
-#include <sciprint.h>
-
-}
-using namespace Ipopt;
-
-class QuadNLP : public TNLP
-{
-	private:
-		Index numVars_;			// Number of variables.
-	
-		Index numConstr_; 		// Number of constraints.
-
-		const Number *qMatrix_ = NULL;	//qMatrix_ is a pointer to matrix of size numVars X numVars_ 
-						// with coefficents of quadratic terms in objective function.
-
-		const Number *lMatrix_ = NULL;//lMatrix_ is a pointer to matrix of size 1*numVars_
-						// with coefficents of linear terms in objective function.	
-	
-		const Number *conMatrix_ = NULL;//conMatrix_ is a pointer to matrix of size numConstr X numVars
-						// with coefficients of terms in a each objective in each row.
-
-		const Number *conUB_= NULL;	//conUB_ is a pointer to a matrix of size of 1*numConstr_
-						// with upper bounds of all constraints.
-
-		const Number *conLB_ = NULL;	//conLB_ is a pointer to a matrix of size of 1*numConstr_ 
-						// with lower bounds of all constraints.
-
-		const Number *varUB_= NULL;	//varUB_ is a pointer to a matrix of size of 1*numVar_ 
-						// with upper bounds of all variables.
-
-		const Number *varLB_= NULL;	//varLB_ is a pointer to a matrix of size of 1*numVar_
-						// with lower bounds of all variables.
-
-		const Number *varGuess_= NULL;	//varGuess_ is a pointer to a matrix of size of 1*numVar_
-						// with initial guess of all variables.
-	
-		Number *finalX_= NULL;		//finalX_ is a pointer to a matrix of size of 1*numVar_
-						// with final value for the primal variables.
-
-		Number *finalZl_= NULL;		//finalZl_ is a pointer to a matrix of size of 1*numVar_
-						// with final values for the lower bound multipliers
-
-		Number *finalZu_= NULL;		//finalZu_ is a pointer to a matrix of size of 1*numVar_
-						// with final values for the upper bound multipliers
-
-		Number *finalLambda_= NULL;	//finalLambda_ is a pointer to a matrix of size of 1*numConstr_
-						// with final values for the upper bound multipliers
-
-		Number finalObjVal_;		//finalObjVal_ is a scalar with the final value of the objective.
-
-		int iter_;			//Number of iteration.
-
-		int status_;			//Solver return status
- 
-		QuadNLP(const QuadNLP&);
-		QuadNLP& operator=(const QuadNLP&);
-	public:
-		/*
-		 * Constructor 
-		*/
-		QuadNLP(Index nV, Index nC, Number *qM, Number *lM, Number *cM, Number *cUB, Number *cLB, Number *vUB, Number *vLB):
-			numVars_(nV),numConstr_(nC),qMatrix_(qM),lMatrix_(lM),conMatrix_(cM),conUB_(cUB),conLB_(cLB),varUB_(vUB),varLB_(vLB),finalX_(0), finalZl_(0), finalZu_(0), finalObjVal_(1e20){	}
-
-
-		/* Go to :
-
-	http://www.coin-or.org/Ipopt/documentation/node23.html#SECTION00053130000000000000
-		For details about these below methods.
-		*/
-		virtual ~QuadNLP();
-		virtual bool get_nlp_info(Index& n, Index& m, Index& nnz_jac_g,
-								  Index& nnz_h_lag, IndexStyleEnum& index_style);
-		virtual bool get_bounds_info(Index n, Number* x_l, Number* x_u,
-									 Index m, Number* g_l, Number* g_u);
-		virtual bool get_starting_point(Index n, bool init_x, Number* x,
-										bool init_z, Number* z_L, Number* z_U,
-										Index m, bool init_lambda,
-										Number* lambda);
-		virtual bool eval_f(Index n, const Number* x, bool new_x, Number& obj_value);
-		virtual bool eval_grad_f(Index n, const Number* x, bool new_x, Number* grad_f);
-		virtual bool eval_g(Index n, const Number* x, bool new_x, Index m, Number* g);
-		virtual bool eval_jac_g(Index n, const Number* x, bool new_x,
-								Index m, Index nele_jac, Index* iRow, Index *jCol,
-								Number* values);
-		virtual bool eval_h(Index n, const Number* x, bool new_x,
-							Number obj_factor, Index m, const Number* lambda,
-							bool new_lambda, Index nele_hess, Index* iRow,
-							Index* jCol, Number* values);
-		virtual void finalize_solution(SolverReturn status,
-						   Index n, const Number* x, const Number* z_L, const Number* z_U,
-						   Index m, const Number* g, const Number* lambda, Number obj_value,
-						   const IpoptData* ip_data,
-						   IpoptCalculatedQuantities* ip_cq);
-		
-		const double * getX();		//Returns a pointer to a matrix of size of 1*numVar 
-						// with final value for the primal variables.
-
-		const double * getZu();		//Returns a pointer to a matrix of size of 1*numVars
-						// with final values for the upper bound multipliers
-
-		const double * getZl();		//Returns a pointer to a matrix of size of 1*numVars
-						// with final values for the upper bound multipliers
-
-		const double * getLambda();	//Returns a pointer to a matrix of size of 1*numConstr
-						// with final values for the constraint multipliers
-
-
-		double getObjVal();		//Returns the output of the final value of the objective.
-
-		double iterCount();		//Returns the iteration count
-
-		int returnStatus();		//Returns the status count
-
-		
-};
-
-#endif __QuadNLP_HPP__
diff --git a/sci_gateway/cpp/README.rst~ b/sci_gateway/cpp/README.rst~
deleted file mode 100644
index e69de29..0000000
--- a/sci_gateway/cpp/README.rst~
+++ /dev/null
diff --git a/sci_gateway/cpp/builder_gateway_cpp.sce~ b/sci_gateway/cpp/builder_gateway_cpp.sce~
deleted file mode 100644
index 225edd8..0000000
--- a/sci_gateway/cpp/builder_gateway_cpp.sce~
+++ /dev/null
@@ -1,149 +0,0 @@
-// 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
-
-mode(-1)
-lines(0)
-
-toolbox_title = "FAMOS";
-
-[a, opt] = getversion();
-Version = opt(2);
-
-path_builder = get_absolute_file_path('builder_gateway_cpp.sce');
-
-tools_path  = path_builder + "../../thirdparty/linux/";
-
-C_Flags=["-w -fpermissive -I"+tools_path+"include/coin -Wl,-rpath="+tools_path+"lib/"+Version+filesep()+" "]
-
-Linker_Flag = ["-L"+tools_path+"lib/"+Version+filesep()+"libSym"+" "+"-L"+tools_path+"lib/"+Version+filesep()+"libipopt" ]
-
-
-//Name of All the Functions
-Function_Names = [
-		//for opening/closing environment and checking if it is open/close
-		"sym_open","sci_sym_open";
-		"sym_close","sci_sym_close";
-		"sym_isEnvActive","sci_sym_isEnvActive";
-		
-		//run time parameters
-		"sym_resetParams","sci_sym_set_defaults";
-		"sym_setIntParam","sci_sym_set_int_param";
-		"sym_getIntParam","sci_sym_get_int_param";
-		"sym_setDblParam","sci_sym_set_dbl_param";
-		"sym_getDblParam","sci_sym_get_dbl_param";
-		"sym_setStrParam","sci_sym_set_str_param";
-		"sym_getStrParam","sci_sym_get_str_param";
-		"sym_getInfinity","sci_sym_getInfinity";
-		
-		//problem loaders
-		"sym_loadProblemBasic","sci_sym_loadProblemBasic";
-		"sym_loadProblem","sci_sym_loadProblem";
-		"sym_loadMPS","sci_sym_load_mps";
-		
-		//basic data
-		"sym_getNumConstr","sci_sym_get_num_int";
-		"sym_getNumVar","sci_sym_get_num_int";
-		"sym_getNumElements","sci_sym_get_num_int";
-		
-		//variable and objective data
-		"sym_isContinuous","sci_sym_isContinuous";
-		"sym_isBinary","sci_sym_isBinary";
-		"sym_isInteger","sci_sym_isInteger";
-		"sym_setContinuous","sci_sym_set_continuous";
-		"sym_setInteger","sci_sym_set_integer";
-		"sym_getVarLower","sci_sym_get_dbl_arr";
-		"sym_getVarUpper","sci_sym_get_dbl_arr";
-		"sym_setVarLower","sci_sym_setVarBound";
-		"sym_setVarUpper","sci_sym_setVarBound";
-		"sym_getObjCoeff","sci_sym_get_dbl_arr";
-		"sym_setObjCoeff","sci_sym_setObjCoeff";
-		"sym_getObjSense","sci_sym_getObjSense";
-		"sym_setObjSense","sci_sym_setObjSense";
-		
-		//constraint data
-		"sym_getRhs","sci_sym_get_dbl_arr";
-		"sym_getConstrRange","sci_sym_get_dbl_arr";
-		"sym_getConstrLower","sci_sym_get_dbl_arr";
-		"sym_getConstrUpper","sci_sym_get_dbl_arr";
-		"sym_setConstrLower","sci_sym_setConstrBound";
-		"sym_setConstrUpper","sci_sym_setConstrBound";
-		"sym_setConstrType","sci_sym_setConstrType";
-		"sym_getMatrix","sci_sym_get_matrix";
-		"sym_getConstrSense","sci_sym_get_row_sense";
-		
-		//add/remove variables and constraints
-		"sym_addConstr","sci_sym_addConstr";
-		"sym_addVar","sci_sym_addVar";
-		"sym_deleteVars","sci_sym_delete_cols";
-		"sym_deleteConstrs","sci_sym_delete_rows";
-		
-		//primal bound
-		"sym_getPrimalBound","sci_sym_getPrimalBound";
-		"sym_setPrimalBound","sci_sym_setPrimalBound";
-		
-		//set preliminary solution
-		"sym_setVarSoln","sci_sym_setColSoln";
-		
-		//solve
-		"sym_solve","sci_sym_solve";
-		
-		//post solve functions
-		"sym_getStatus","sci_sym_get_status";
-		"sym_isOptimal","sci_sym_get_solver_status";
-		"sym_isInfeasible","sci_sym_get_solver_status";
-		"sym_isAbandoned","sci_sym_get_solver_status";
-		"sym_isIterLimitReached","sci_sym_get_solver_status";
-		"sym_isTimeLimitReached","sci_sym_get_solver_status";
-		"sym_isTargetGapAchieved","sci_sym_get_solver_status";
-		"sym_getVarSoln","sci_sym_getVarSoln";
-		"sym_getObjVal","sci_sym_getObjVal";
-		"sym_getIterCount","sci_sym_get_iteration_count";
-		"sym_getConstrActivity","sci_sym_getRowActivity";
-
-		//QP function
-		"solveqp","sci_solveqp"
-	];
-
-//Name of all the files to be compiled
-Files = [
-		"globals.cpp",
-		"sci_iofunc.hpp",
-		"sci_iofunc.cpp",
-		"sci_sym_openclose.cpp",
-		"sci_solver_status_query_functions.cpp",
-		"sci_sym_solve.cpp",
-		"sci_sym_loadproblem.cpp",
-		"sci_sym_isenvactive.cpp",
-		"sci_sym_load_mps.cpp",
-		"sci_vartype.cpp",
-		"sci_sym_getinfinity.cpp",
-		"sci_sym_solution.cpp",
-		"sym_data_query_functions.cpp"
-		"sci_sym_set_variables.cpp",
-		"sci_sym_setobj.cpp",
-		"sci_sym_varbounds.cpp",
-		"sci_sym_rowmod.cpp",
-		"sci_sym_set_indices.cpp",
-		"sci_sym_addrowcol.cpp",
-		"sci_sym_primalbound.cpp",
-		"sci_sym_setcolsoln.cpp",
-		"sci_sym_getrowact.cpp",
-		"sci_sym_getobjsense.cpp",
-		"sci_sym_remove.cpp",
-		"sci_QuadNLP.cpp",
-		"QuadNLP.hpp",
-		"sci_ipopt.cpp"
-				
-	]
-
-tbx_build_gateway(toolbox_title,Function_Names,Files,get_absolute_file_path("builder_gateway_cpp.sce"), [], Linker_Flag, C_Flags, [], "g++");
-
-clear WITHOUT_AUTO_PUTLHSVAR toolbox_title Function_Names Files Linker_Flag C_Flags;
diff --git a/sci_gateway/cpp/sci_QuadNLP.cpp~ b/sci_gateway/cpp/sci_QuadNLP.cpp~
deleted file mode 100644
index 99987a2..0000000
--- a/sci_gateway/cpp/sci_QuadNLP.cpp~
+++ /dev/null
@@ -1,253 +0,0 @@
-/*
- * Quadratic Programming Toolbox for Scilab using IPOPT library
- * Authors :
-	Sai Kiran
-	Keyur Joshi
-	Iswarya
- */
-
-#include "QuadNLP.hpp"
-#include "IpIpoptData.hpp"
-
-extern "C"{
-#include <api_scilab.h>
-#include <Scierror.h>
-#include <BOOL.h>
-#include <localization.h>
-#include <sciprint.h>
-
-
-double x_static,i, *op_obj_x = NULL,*op_obj_value = NULL;
-
-using namespace Ipopt;
-
-QuadNLP::~QuadNLP()
-		 {
-			free(finalX_);
-			free(finalZl_);
-			free(finalZu_);}
-
-//get NLP info such as number of variables,constraints,no.of elements in jacobian and hessian to allocate memory
-bool QuadNLP::get_nlp_info(Index& n, Index& m, Index& nnz_jac_g, Index& nnz_h_lag, IndexStyleEnum& index_style){
-	n=numVars_; // Number of variables
-	m=numConstr_; // Number of constraints
-	nnz_jac_g = n*m; // No. of elements in Jacobian of constraints 
-	nnz_h_lag = n*(n+1)/2; // No. of elements in lower traingle of Hessian of the Lagrangian.
-	index_style=C_STYLE; // Index style of matrices
-	return true;
-	}
-
-//get variable and constraint bound info
-bool QuadNLP::get_bounds_info(Index n, Number* x_l, Number* x_u, Index m, Number* g_l, Number* g_u){
-	
-	unsigned int i;
-	for(i=0;i<n;i++){
-		x_l[i]=varLB_[i];
-		x_u[i]=varUB_[i];
-		}
-
-	for(i=0;i<m;i++){
-		g_l[i]=conLB_[i];
-		g_u[i]=conUB_[i];
-		}
-	return true;
-	}
-
-//get value of objective function at vector x
-bool QuadNLP::eval_f(Index n, const Number* x, bool new_x, Number& obj_value){
-	unsigned int i,j;
-	obj_value=0;
-
-	for (i=0;i<=n;i++){
-		for (j=0;j<=n;j++){
-			obj_value+=0.5*x[i]*x[j]*qMatrix_[n*i+j];		
-			}
-		obj_value+=x[i]*lMatrix_[i];
-		}
-	return true;
-	}
-
-//get value of gradient of objective function at vector x.
-bool QuadNLP::eval_grad_f(Index n, const Number* x, bool new_x, Number* grad_f){
-	unsigned int i,j;
-	for(i=0;i<n;i++)
-	{
-		grad_f[i]=lMatrix_[i];
-		for(j=0;j<n;j++)
-			{
-				grad_f[i]+=(qMatrix_[n*i+j])*x[j];
-			}
-	}
-	return true;
-}
-
-//Get the values of constraints at vector x.
-bool QuadNLP::eval_g(Index n, const Number* x, bool new_x, Index m, Number* g){
-	unsigned int i,j;
-	for(i=0;i<m;i++)
-	{
-		g[i]=0;
-		for(j=0;j<n;j++)
-		{
-			g[i]+=x[j]*conMatrix_[i+j*m];	
-		}
-	}
-	return true;
-}
-
-// This method sets initial values for required vectors . For now we are assuming 0 to all values. 
-bool QuadNLP::get_starting_point(Index n, bool init_x, Number* x,
-				 bool init_z, Number* z_L, Number* z_U,
-				 Index m, bool init_lambda,
-				 Number* lambda){
-	if (init_x == true){ //we need to set initial values for vector x
-		for (Index var=0;var<n;var++)
-			x[var]=varGuess_[var];//initialize with 0 or we can change.
-		}
-
-	if (init_z == true){ //we need to provide initial values for vector bound multipliers
-		for (Index var=0;var<n;++var){
-			z_L[var]=0.0; //initialize with 0 or we can change.
-			z_U[var]=0.0;//initialize with 0 or we can change.
-			}
-		}
-	
-	if (init_lambda == true){ //we need to provide initial values for lambda values.
-		for (Index var=0;var<m;++var){
-			lambda[var]=0.0; //initialize with 0 or we can change.
-			}
-		}
-
-	return true;
-	}
-/* Return either the sparsity structure of the Jacobian of the constraints, or the values for the Jacobian of the constraints at the point x.
-
-*/ 
-bool QuadNLP::eval_jac_g(Index n, const Number* x, bool new_x,
-			 Index m, Index nele_jac, Index* iRow, Index *jCol,
-			 Number* values){
-	
-	//It asked for structure of jacobian.
-	if (values==NULL){ //Structure of jacobian (full structure)
-		int index=0;
-		for (int var=0;var<m;++var)//no. of constraints
-			for (int flag=0;flag<n;++flag){//no. of variables
-				iRow[index]=var;
-				jCol[index]=flag;
-				index++;
-				}
-		}
-	//It asked for values
-	else { 
-		int index=0;
-		for (int var=0;var<m;++var)
-			for (int flag=0;flag<n;++flag)
-				values[index++]=conMatrix_[var+flag*m];
-		}
-	return true;
-	}
-
-/*
- * Return either the sparsity structure of the Hessian of the Lagrangian, 
- * or the values of the Hessian of the Lagrangian  for the given values for
- * x,lambda,obj_factor.
-*/
-bool QuadNLP::eval_h(Index n, const Number* x, bool new_x,
-		     Number obj_factor, Index m, const Number* lambda,
-		     bool new_lambda, Index nele_hess, Index* iRow,
-		     Index* jCol, Number* values){
-
-	if (values==NULL){
-		Index idx=0;
-		for (Index row = 0; row < n; row++) {
-			for (Index col = 0; col <= row; col++) {
-				iRow[idx] = row;
-				jCol[idx] = col;
-				idx++;
-		  		}
-			}
-		}
-	else {
-		Index index=0;
-		for (Index row=0;row < n;++row){
-			for (Index col=0; col <= row; ++col){
-				values[index++]=obj_factor*(qMatrix_[n*row+col]);
-				}
-			}
-		}
-	return true;
-	}
-
-
-void QuadNLP::finalize_solution(SolverReturn status,
-				Index n, const Number* x, const Number* z_L, const Number* z_U,
-				Index m, const Number* g, const Number* lambda, Number obj_value,
-				const IpoptData* ip_data,
-				IpoptCalculatedQuantities* ip_cq){
-	
-	finalX_ = (double*)malloc(sizeof(double) * numVars_ * 1);
-	for (Index i=0; i<n; i++) 
-	{
-    		 finalX_[i] = x[i];
-	}
-	
-	finalZl_ = (double*)malloc(sizeof(double) * numVars_ * 1);
-	for (Index i=0; i<n; i++) 
-	{
-    		 finalZl_[i] = z_L[i];
-	}
-
-	finalZu_ = (double*)malloc(sizeof(double) * numVars_ * 1);
-	for (Index i=0; i<n; i++) 
-	{
-    		 finalZu_[i] = z_U[i];
-	}
-
-	finalLambda_ = (double*)malloc(sizeof(double) * numConstr_ * 1);
-	for (Index i=0; i<m; i++) 
-	{
-    		 finalLambda_[i] = lambda[i];
-	}
-
-	iter_ = ip_data->iter_count();
-	finalObjVal_ = obj_value;
-	status_ = status;
-	
-   }
-
-	const double * QuadNLP::getX()
-	{	
-		return finalX_;
-	}
-
-	const double * QuadNLP::getZl()
-	{	
-		return finalZl_;
-	}
-
-	const double * QuadNLP::getZu()
-	{	
-		return finalZu_;
-	}
-
-	const double * QuadNLP::getLambda()
-	{	
-		return finalLambda_;
-	}
-
-	double QuadNLP::getObjVal()
-	{	
-		return finalObjVal_;
-	}
-
-	double QuadNLP::iterCount()
-	{	
-		return (double)iter_;
-	}
-
-	int QuadNLP::returnStatus()
-	{	
-		return status_;
-	}
-
-}
diff --git a/sci_gateway/cpp/sci_ipopt.cpp~ b/sci_gateway/cpp/sci_ipopt.cpp~
deleted file mode 100644
index 8d62b21..0000000
--- a/sci_gateway/cpp/sci_ipopt.cpp~
+++ /dev/null
@@ -1,409 +0,0 @@
-/*
- * Quadratic Programming Toolbox for Scilab using IPOPT library
- * Authors :
-	Sai Kiran
-	Keyur Joshi
-	Iswarya
- */
-
-
-#include "sci_iofunc.hpp"
-#include "IpIpoptApplication.hpp"
-#include "QuadNLP.hpp"
-
-extern "C"{
-#include <api_scilab.h>
-#include <Scierror.h>
-#include <BOOL.h>
-#include <localization.h>
-#include <sciprint.h>
-
-int j;
-double *op_x, *op_obj,*p;
-
-bool readSparse(int arg,int *iRows,int *iCols,int *iNbItem,int** piNbItemRow, int** piColPos, double** pdblReal){
-	SciErr sciErr;
-	int* piAddr = NULL;
-	int iType   = 0;
-	int iRet    = 0;
-	sciErr = getVarAddressFromPosition(pvApiCtx, arg, &piAddr);
-	if(sciErr.iErr)	{
-		printError(&sciErr, 0);
-		return false;
-		}
-	sciprint("\ndone\n");
-	if(isSparseType(pvApiCtx, piAddr)){
-		sciprint("done\n");
-		sciErr =getSparseMatrix(pvApiCtx, piAddr, iRows, iCols, iNbItem, piNbItemRow, piColPos, pdblReal);
-		if(sciErr.iErr)	{
-			printError(&sciErr, 0);
-			return false;
-			}
-		}
-
-	else {
-		sciprint("\nSparse matrix required\n");
-		return false;
-		}
-	return true;
-	}
-
-int sci_solveqp(char *fname)
-{
-	
-	CheckInputArgument(pvApiCtx, 10, 10); // We need total 10 input arguments.
-	CheckOutputArgument(pvApiCtx, 7, 7);
-	
-	// Error management variable
-	SciErr sciErr;
-	int retVal=0, *piAddressVarQ = NULL,*piAddressVarP = NULL,*piAddressVarCM = NULL,*piAddressVarCUB = NULL,*piAddressVarCLB = NULL, 		*piAddressVarLB = NULL,*piAddressVarUB = NULL,*piAddressVarG = NULL;
-	double *QItems=NULL,*PItems=NULL,*ConItems=NULL,*conUB=NULL,*conLB=NULL,*varUB=NULL,*varLB=NULL,*init_guess = NULL,x,f,iter;
-	static unsigned int nVars = 0,nCons = 0;
-	unsigned int temp1 = 0,temp2 = 0;
-
-
-	////////// Manage the input argument //////////
-	
-	
-	//Number of Variables
-	getIntFromScilab(1,&nVars);
-
-	//Number of Constraints
-	getIntFromScilab(2,&nCons);
-
-	temp1 = nVars;
-	temp2 = nCons;
-
-	//Q matrix from scilab
-	/* get Address of inputs */
-	sciErr = getVarAddressFromPosition(pvApiCtx, 3, &piAddressVarQ);
-	if (sciErr.iErr)
-	{
-		printError(&sciErr, 0);
-		return 0;
-	}
-
-	/* Check that the first input argument is a real matrix (and not complex) */
-	if ( !isDoubleType(pvApiCtx, piAddressVarQ) ||  isVarComplex(pvApiCtx, piAddressVarQ) )
-	{
-		Scierror(999, "%s: Wrong type for input argument #%d: A real matrix expected.\n", fname, 3);
-		return 0;
-	}
-
-	/* get matrix */
-	sciErr = getMatrixOfDouble(pvApiCtx, piAddressVarQ, &temp1, &temp1, &QItems);
-	if (sciErr.iErr)
-	{
-	printError(&sciErr, 0);
-	return 0;
-	}
-	
-	//P matrix from scilab
-	/* get Address of inputs */
-	sciErr = getVarAddressFromPosition(pvApiCtx, 4, &piAddressVarP);
-	if (sciErr.iErr)
-	{
-	printError(&sciErr, 0);
-	return 0;
-	}
-
-	/* Check that the first input argument is a real matrix (and not complex) */
-	if ( !isDoubleType(pvApiCtx, piAddressVarP) ||  isVarComplex(pvApiCtx, piAddressVarP) )
-	{
-		Scierror(999, "%s: Wrong type for input argument #%d: A real matrix expected.\n", fname, 4);
-		return 0;
-	}
-	
-	temp1 = 1;
-	temp2 = nVars; 
-	/* get matrix */
-	sciErr = getMatrixOfDouble(pvApiCtx, piAddressVarP, &temp1,&temp2, &PItems);
-	if (sciErr.iErr)
-	{
-		printError(&sciErr, 0);
-		return 0;
-	}
-
-	if (nCons!=0)
-	{
-		//conMatrix matrix from scilab
-		/* get Address of inputs */
-		sciErr = getVarAddressFromPosition(pvApiCtx, 5, &piAddressVarCM);
-		if (sciErr.iErr)
-		{
-		printError(&sciErr, 0);
-		return 0;
-		}
-
-		/* Check that the first input argument is a real matrix (and not complex) */
-		if ( !isDoubleType(pvApiCtx, piAddressVarCM) ||  isVarComplex(pvApiCtx, piAddressVarCM) )
-		{
-		Scierror(999, "%s: Wrong type for input argument #%d: A real matrix expected.\n", fname, 5);
-		return 0;
-		}
-		temp1 = nCons;
-		temp2 = nVars;
-
-		/* get matrix */
-		sciErr = getMatrixOfDouble(pvApiCtx, piAddressVarCM,&temp1, &temp2, &ConItems);
-		if (sciErr.iErr)
-		{
-		printError(&sciErr, 0);
-		return 0;
-		}
-
-
-		//conLB matrix from scilab
-		/* get Address of inputs */
-		sciErr = getVarAddressFromPosition(pvApiCtx, 6, &piAddressVarCLB);
-		if (sciErr.iErr)
-		{
-		printError(&sciErr, 0);
-		return 0;
-		}
-
-		/* Check that the first input argument is a real matrix (and not complex) */
-		if ( !isDoubleType(pvApiCtx, piAddressVarCLB) ||  isVarComplex(pvApiCtx, piAddressVarCLB) )
-		{
-		Scierror(999, "%s: Wrong type for input argument #%d: A real matrix expected.\n", fname, 6);
-		return 0;
-		}
-		temp1 = nCons;
-		temp2 = 1;
-
-		/* get matrix */
-		sciErr = getMatrixOfDouble(pvApiCtx, piAddressVarCLB,&temp1, &temp2, &conLB);
-		if (sciErr.iErr)
-		{
-		printError(&sciErr, 0);
-		return 0;
-		}
-		
-		//conUB matrix from scilab
-		/* get Address of inputs */
-		sciErr = getVarAddressFromPosition(pvApiCtx, 7, &piAddressVarCUB);
-		if (sciErr.iErr)
-		{
-		printError(&sciErr, 0);
-		return 0;
-		}
-
-		/* Check that the first input argument is a real matrix (and not complex) */
-		if ( !isDoubleType(pvApiCtx, piAddressVarCUB) ||  isVarComplex(pvApiCtx, piAddressVarCUB) )
-		{
-		Scierror(999, "%s: Wrong type for input argument #%d: A real matrix expected.\n", fname, 7);
-		return 0;
-		}
-
-		temp1 = nCons;
-		temp2 = 1;
-
-		/* get matrix */
-		sciErr = getMatrixOfDouble(pvApiCtx, piAddressVarCUB,&temp1, &temp2, &conUB);
-		if (sciErr.iErr)
-		{
-		printError(&sciErr, 0);
-		return 0;
-		}
-		
-	}
-
-	//varLB matrix from scilab
-	/* get Address of inputs */
-	sciErr = getVarAddressFromPosition(pvApiCtx, 8, &piAddressVarLB);
-	if (sciErr.iErr)
-	{
-	printError(&sciErr, 0);
-	return 0;
-	}
-
-	/* Check that the first input argument is a real matrix (and not complex) */
-	if ( !isDoubleType(pvApiCtx, piAddressVarLB) ||  isVarComplex(pvApiCtx, piAddressVarLB) )
-	{
-		Scierror(999, "%s: Wrong type for input argument #%d: A real matrix expected.\n", fname, 8);
-		return 0;
-	}
-	temp1 = 1;
-	temp2 = nVars;
-
-	/* get matrix */
-	sciErr = getMatrixOfDouble(pvApiCtx, piAddressVarLB, &temp1,&temp2, &varLB);
-	if (sciErr.iErr)
-	{
-		printError(&sciErr, 0);
-		return 0;
-	}
-
-	//varUB matrix from scilab
-	/* get Address of inputs */
-	sciErr = getVarAddressFromPosition(pvApiCtx, 9, &piAddressVarUB);
-	if (sciErr.iErr)
-	{
-		printError(&sciErr, 0);
-		return 0;
-	}
-	/* Check that the first input argument is a real matrix (and not complex) */
-	if ( !isDoubleType(pvApiCtx, piAddressVarUB) ||  isVarComplex(pvApiCtx, piAddressVarUB) )
-	{
-		Scierror(999, "%s: Wrong type for input argument #%d: A real matrix expected.\n", fname, 9);
-		return 0;
-	}
-
-	temp1 = 1;
-	temp2 = nVars;
-
-	/* get matrix */
-	sciErr = getMatrixOfDouble(pvApiCtx, piAddressVarUB, &temp1,&temp2, &varUB);
-	if (sciErr.iErr)
-	{
-		printError(&sciErr, 0);
-		return 0;
-	}
-	
-	/* get matrix */
-	sciErr = getMatrixOfDouble(pvApiCtx, piAddressVarLB, &temp1,&temp2, &varLB);
-	if (sciErr.iErr)
-	{
-		printError(&sciErr, 0);
-		return 0;
-	}
-
-	//Initial Value of variables from scilab
-	/* get Address of inputs */
-	sciErr = getVarAddressFromPosition(pvApiCtx, 10, &piAddressVarG);
-	if (sciErr.iErr)
-	{
-		printError(&sciErr, 0);
-		return 0;
-	}
-	/* Check that the first input argument is a real matrix (and not complex) */
-	if ( !isDoubleType(pvApiCtx, piAddressVarG) ||  isVarComplex(pvApiCtx, piAddressVarG) )
-	{
-		Scierror(999, "%s: Wrong type for input argument #%d: A real matrix expected.\n", fname, 10);
-		return 0;
-	}
-
-	temp1 = 1;
-	temp2 = nVars;
-
-	/* get matrix */
-	sciErr = getMatrixOfDouble(pvApiCtx, piAddressVarG, &temp1,&temp2, &init_guess);
-	if (sciErr.iErr)
-	{
-		printError(&sciErr, 0);
-		return 0;
-	}
-	
-		using namespace Ipopt;
-
-		SmartPtr<QuadNLP> Prob = new QuadNLP(nVars,nCons,QItems,PItems,ConItems,conUB,conLB,varUB,varLB);
-		SmartPtr<IpoptApplication> app = IpoptApplicationFactory();
-  		app->RethrowNonIpoptException(true);
-
-		// Change some options
-		// Note: The following choices are only examples, they might not be
-		//       suitable for your optimization problem.
-		app->Options()->SetNumericValue("tol", 1e-7);
-		app->Options()->SetStringValue("mu_strategy", "adaptive");
-
-		// Indicates whether all equality constraints are linear 
-		app->Options()->SetStringValue("jac_c_constant", "yes");
-		// Indicates whether all inequality constraints are linear 
-		app->Options()->SetStringValue("jac_d_constant", "yes");	
-		// Indicates whether the problem is a quadratic problem 
-		app->Options()->SetStringValue("hessian_constant", "yes");
-	
-		// Initialize the IpoptApplication and process the options
-		ApplicationReturnStatus status;
-	 	status = app->Initialize();
-		if (status != Solve_Succeeded) {
-		  	sciprint("\n*** Error during initialization!\n");
-			return0toScilab();
-	   	 return (int) status;
-	 	 }
-		 // Ask Ipopt to solve the problem
-		
-		 status = app->OptimizeTNLP(Prob);
-
-		double *fX = Prob->getX();
-		double ObjVal = Prob->getObjVal();
-		double *Zl = Prob->getZl();
-		double *Zu = Prob->getZu();
-		double *Lambda = Prob->getLambda();
-		double iteration = Prob->iterCount();
-		int stats = Prob->returnStatus();
-		sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 1, nVars, fX);
-		if (sciErr.iErr)
-		{
-			printError(&sciErr, 0);
-			return 0;
-		}
-
-		sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 2,1,1,&ObjVal);
-		if (sciErr.iErr)
-		{
-			printError(&sciErr, 0);
-			return 0;
-		}
-
-		sciErr = createMatrixOfInteger32(pvApiCtx, nbInputArgument(pvApiCtx) + 3,1,1,&stats);
-		if (sciErr.iErr)
-		{
-			printError(&sciErr, 0);
-			return 0;
-		}
-
-		sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 4,1,1,&iteration);
-		if (sciErr.iErr)
-		{
-			printError(&sciErr, 0);
-			return 0;
-		}
-
-		sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 5, 1, nVars, Zl);
-		if (sciErr.iErr)
-		{
-			printError(&sciErr, 0);
-			return 0;
-		}
-
-		sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 6, 1, nVars, Zu);
-		if (sciErr.iErr)
-		{
-			printError(&sciErr, 0);
-			return 0;
-		}
-		
-		sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 7, 1, nCons, Lambda);
-		if (sciErr.iErr)
-		{
-			printError(&sciErr, 0);
-			return 0;
-		}
-		
-
-		AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1;
-		AssignOutputVariable(pvApiCtx, 2) = nbInputArgument(pvApiCtx) + 2;		
-		AssignOutputVariable(pvApiCtx, 3) = nbInputArgument(pvApiCtx) + 3;
-		AssignOutputVariable(pvApiCtx, 4) = nbInputArgument(pvApiCtx) + 4;
-		AssignOutputVariable(pvApiCtx, 5) = nbInputArgument(pvApiCtx) + 5;
-		AssignOutputVariable(pvApiCtx, 6) = nbInputArgument(pvApiCtx) + 6;
-		AssignOutputVariable(pvApiCtx, 7) = nbInputArgument(pvApiCtx) + 7;	
-
-  		// As the SmartPtrs go out of scope, the reference count
-  		// will be decremented and the objects will automatically
-  		// be deleted.
-	
-		
-	return 0;
-	}
-
-}
-
-/*
-hessian_constan
-jacobian _constant
-
-j_s_d constant : yes
-*/
-
diff --git a/sci_gateway/cpp/sci_sym_solve.cpp~ b/sci_gateway/cpp/sci_sym_solve.cpp~
deleted file mode 100644
index 4abb268..0000000
--- a/sci_gateway/cpp/sci_sym_solve.cpp~
+++ /dev/null
@@ -1,49 +0,0 @@
-/*
- * Implementation Symphony Tool Box for Scilab
- * Contains sym_solve function
- * Author : Sai Kiran
- */
-
-#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 <BOOL.h>
-#include <localization.h>
-#include <sciprint.h>
-#include <stdio.h>
-int process_ret_val(int);
-
-int sci_sym_solve(char *fname, unsigned long fname_len){
-	
-	int status=0; 
-  
-	//check whether we have no input and one output argument or not
-	CheckInputArgument(pvApiCtx, 0, 0) ;//no input argument
-	CheckOutputArgument(pvApiCtx, 1, 1) ;//one output argument
-
-	// Check environment
-	if(global_sym_env==NULL)
-		sciprint("Error: Symphony environment is not initialized.\n");
-	else {// There is an environment opened
-		double time_limit = -1.0;
-		status = sym_get_dbl_param(global_sym_env,"time_limit",&time_limit);
-
-		if (status == FUNCTION_TERMINATED_NORMALLY) {
-			if ( time_limit < 0.0 )
-				sciprint("\nNote: There is no limit on time.\n");
-			else sciprint("\nNote: Time limit has been set to %lf.\n",time_limit);
-			status=process_ret_val(sym_solve(global_sym_env));// Call function	
-			}
-		else {
-			sciprint("\nUnable to read time limit.\n");
-			status = 1; //Error state
-			}
-		}
-	// Return result to scilab
-	return returnDoubleToScilab(status);
-	}
-}
diff --git a/tests/unit_tests/README.rst~ b/tests/unit_tests/README.rst~
deleted file mode 100644
index e69de29..0000000
--- a/tests/unit_tests/README.rst~
+++ /dev/null
diff --git a/tests/unit_tests/qpipopt_base.tst~ b/tests/unit_tests/qpipopt_base.tst~
deleted file mode 100644
index 9de0d6b..0000000
--- a/tests/unit_tests/qpipopt_base.tst~
+++ /dev/null
@@ -1,76 +0,0 @@
-// 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
-
-// <-- JVM NOT MANDATORY -->
-// <-- ENGLISH IMPOSED -->
-
-
-//
-// assert_close --
-//   Returns 1 if the two real matrices computed and expected are close,
-//   i.e. if the relative distance between computed and expected is lesser than epsilon.
-// Arguments
-//   computed, expected : the two matrices to compare
-//   epsilon : a small number
-//
-function flag = assert_close ( computed, expected, epsilon )
-  if expected==0.0 then
-    shift = norm(computed-expected);
-  else
-    shift = norm(computed-expected)/norm(expected);
-  end
-//  if shift < epsilon then
-//    flag = 1;
-//  else
-//    flag = 0;
-//  end
-//  if flag <> 1 then pause,end
-    flag = assert_checktrue ( shift < epsilon );
-endfunction
-//
-// assert_equal --
-//   Returns 1 if the two real matrices computed and expected are equal.
-// Arguments
-//   computed, expected : the two matrices to compare
-//   epsilon : a small number
-//
-//function flag = assert_equal ( computed , expected )
-//  if computed==expected then
-//    flag = 1;
-//  else
-//    flag = 0;
-//  end
-//  if flag <> 1 then pause,end
-//endfunction
-
-///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.
-Q = [1 -1; -1 2];
-p = [-2; -6];
-conMatrix = [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,Q,p,lb,ub,conMatrix,conLB,conUB)
-
-assert_close ( x , [0.6666667 1.3333333]' , 1.e-7 );
-assert_close ( f , [ - 8.2222223] , 1.e-7 );
-
-assert_checkequal( exitflag , 0 );
diff --git a/tests/unit_tests/qpipoptmat_base .tst~ b/tests/unit_tests/qpipoptmat_base .tst~
deleted file mode 100644
index 9de0d6b..0000000
--- a/tests/unit_tests/qpipoptmat_base .tst~
+++ /dev/null
@@ -1,76 +0,0 @@
-// 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
-
-// <-- JVM NOT MANDATORY -->
-// <-- ENGLISH IMPOSED -->
-
-
-//
-// assert_close --
-//   Returns 1 if the two real matrices computed and expected are close,
-//   i.e. if the relative distance between computed and expected is lesser than epsilon.
-// Arguments
-//   computed, expected : the two matrices to compare
-//   epsilon : a small number
-//
-function flag = assert_close ( computed, expected, epsilon )
-  if expected==0.0 then
-    shift = norm(computed-expected);
-  else
-    shift = norm(computed-expected)/norm(expected);
-  end
-//  if shift < epsilon then
-//    flag = 1;
-//  else
-//    flag = 0;
-//  end
-//  if flag <> 1 then pause,end
-    flag = assert_checktrue ( shift < epsilon );
-endfunction
-//
-// assert_equal --
-//   Returns 1 if the two real matrices computed and expected are equal.
-// Arguments
-//   computed, expected : the two matrices to compare
-//   epsilon : a small number
-//
-//function flag = assert_equal ( computed , expected )
-//  if computed==expected then
-//    flag = 1;
-//  else
-//    flag = 0;
-//  end
-//  if flag <> 1 then pause,end
-//endfunction
-
-///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.
-Q = [1 -1; -1 2];
-p = [-2; -6];
-conMatrix = [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,Q,p,lb,ub,conMatrix,conLB,conUB)
-
-assert_close ( x , [0.6666667 1.3333333]' , 1.e-7 );
-assert_close ( f , [ - 8.2222223] , 1.e-7 );
-
-assert_checkequal( exitflag , 0 );