diff options
Diffstat (limited to 'help/en_US/symphony_mat.xml')
| -rw-r--r-- | help/en_US/symphony_mat.xml | 202 |
1 files changed, 0 insertions, 202 deletions
diff --git a/help/en_US/symphony_mat.xml b/help/en_US/symphony_mat.xml deleted file mode 100644 index 4f6e9c9..0000000 --- a/help/en_US/symphony_mat.xml +++ /dev/null @@ -1,202 +0,0 @@ -<?xml version="1.0" encoding="UTF-8"?> - -<!-- - * - * This help file was generated from symphony_mat.sci using help_from_sci(). - * - --> - -<refentry version="5.0-subset Scilab" xml:id="symphony_mat" xml:lang="en" - xmlns="http://docbook.org/ns/docbook" - xmlns:xlink="http://www.w3.org/1999/xlink" - xmlns:svg="http://www.w3.org/2000/svg" - xmlns:ns3="http://www.w3.org/1999/xhtml" - xmlns:mml="http://www.w3.org/1998/Math/MathML" - xmlns:scilab="http://www.scilab.org" - xmlns:db="http://docbook.org/ns/docbook"> - - <refnamediv> - <refname>symphony_mat</refname> - <refpurpose>Solves a mixed integer linear programming constrained optimization problem in intlinprog format.</refpurpose> - </refnamediv> - - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - xopt = symphony_mat(f,intcon,A,b) - xopt = symphony_mat(f,intcon,A,b,Aeq,beq) - xopt = symphony_mat(f,intcon,A,b,Aeq,beq,lb,ub) - xopt = symphony_mat(f,intcon,A,b,Aeq,beq,lb,ub,options) - [xopt,fopt,status,output] = symphony_mat( ... ) - - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>f :</term> - <listitem><para> a 1xn matrix of doubles, where n is number of variables, contains coefficients of the variables in the objective</para></listitem></varlistentry> - <varlistentry><term>intcon :</term> - <listitem><para> 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</para></listitem></varlistentry> - <varlistentry><term>A :</term> - <listitem><para> 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</para></listitem></varlistentry> - <varlistentry><term>b :</term> - <listitem><para> 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</para></listitem></varlistentry> - <varlistentry><term>Aeq :</term> - <listitem><para> 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</para></listitem></varlistentry> - <varlistentry><term>beq :</term> - <listitem><para> 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.</para></listitem></varlistentry> - <varlistentry><term>lb :</term> - <listitem><para> Lower bounds, specified as a vector or array of doubles. lb represents the lower bounds elementwise in lb ≤ x ≤ ub.</para></listitem></varlistentry> - <varlistentry><term>ub :</term> - <listitem><para> Upper bounds, specified as a vector or array of doubles. ub represents the upper bounds elementwise in lb ≤ x ≤ ub.</para></listitem></varlistentry> - <varlistentry><term>options :</term> - <listitem><para> a 1xq marix of string, provided to set the paramters in symphony</para></listitem></varlistentry> - <varlistentry><term>xopt :</term> - <listitem><para> a 1xn matrix of doubles, the computed solution of the optimization problem</para></listitem></varlistentry> - <varlistentry><term>fopt :</term> - <listitem><para> a 1x1 matrix of doubles, the function value at x</para></listitem></varlistentry> - <varlistentry><term>output :</term> - <listitem><para> The output data structure contains detailed informations about the optimization process.</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -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 - </para> - <para> -<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> - </para> - <para> -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. - </para> - <para> -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -// 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,iter] = symphony_mat(c,intcon,[],[],Aeq,beq,lb,ub); - - ]]></programlisting> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -// 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 -// 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); - - ]]></programlisting> -</refsection> - -<refsection> - <title>Authors</title> - <simplelist type="vert"> - <member>Keyur Joshi, Saikiran, Iswarya, Harpreet Singh</member> - </simplelist> -</refsection> -</refentry> |