initial add

1 files changed, 351 insertions, 0 deletions

diff --git a/build/Scilab/intfminunc.sci b/build/Scilab/intfminunc.sci
new file mode 100644
index 0000000..5141017
--- /dev/null
+++ b/build/Scilab/intfminunc.sci
@@ -0,0 +1,351 @@
+// Copyright (C) 2015 - IIT Bombay - FOSSEE
+//
+// 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
+// Author: Harpreet Singh, Pranav Deshpande and Akshay Miterani
+// Organization: FOSSEE, IIT Bombay
+// Email: toolbox@scilab.in
+
+function [xopt,fopt,exitflag,gradient,hessian] = intfminunc (varargin)
+	// Solves an unconstrainted multi-variable mixed integer non linear programming optimization problem
+	//
+	//   Calling Sequence
+	//   xopt = intfminunc(f,x0)
+	//   xopt = intfminunc(f,x0,intcon)
+	//   xopt = intfminunc(f,x0,intcon,options)
+	//   [xopt,fopt] = intfminunc(.....)
+	//   [xopt,fopt,exitflag]= intfminunc(.....)
+	//   [xopt,fopt,exitflag,gradient,hessian]= intfminunc(.....)
+	//
+	//   Parameters
+	//   f : a function, representing the objective function of the problem 
+	//   x0 : a vector of doubles, containing the starting of variables.
+	//	 intcon : a vector of integers, represents which variables are constrained to be integers
+	//   options: a list, containing the option for user to specify. See below for details.
+	//   xopt : a vector of doubles, the computed solution of the optimization problem.
+	//   fopt : a scalar of double, the function value at x. 
+	//   exitflag : a scalar of integer, containing the flag which denotes the reason for termination of algorithm. See below for details.
+  //   gradient : a vector of doubles, containing the Objective's gradient of the solution.
+  //   hessian  : a matrix of doubles, containing the Objective's hessian of the solution.
+	//
+	//   Description
+	//   Search the minimum of a multi-variable mixed integer non linear programming unconstrained optimization problem specified by :
+	//   Find the minimum of f(x) such that 
+	//
+	//   <latex>
+	//    \begin{eqnarray}
+	//    &\mbox{min}_{x}
+	//    & f(x)
+  //    & x_i \in \!\, \mathbb{Z}, i \in \!\, I
+	//    \end{eqnarray}
+	//   </latex>
+	//
+	//   The routine calls Bonmin for solving the Un-constrained Optimization problem, Bonmin is a library written in C++.
+	//
+	// The options allows the user to set various parameters of the Optimization problem. 
+	// It should be defined as type "list" and contains the following fields.
+	// <itemizedlist>
+	//   <listitem>Syntax : options= list("IntegerTolerance", [---], "MaxNodes", [---], "CpuTime", [---], "AllowableGap", [---], "MaxIter", [---]);</listitem>
+	//   <listitem>IntegerTolerance : a Scalar, containing the Integer tolerance value that the solver should take.</listitem>
+	//   <listitem>MaxNodes : a Scalar, containing the maximum nodes that the solver should make.</listitem>
+	//	 <listitem>MaxIter : a Scalar, containing the Maximum Number of Iteration that the solver should take.</listitem>
+	//   <listitem>AllowableGap : a Scalar, containing the allowable gap value that the solver should take.</listitem>
+	//   <listitem>CpuTime : a Scalar, containing the Maximum amount of CPU Time that the solver should take.</listitem>
+  //   <listitem>gradobj : a string, to turn on or off the user supplied objective gradient.</listitem>
+  //   <listitem>hessian : a Scalar, to turn on or off the user supplied objective hessian.</listitem>
+  //   <listitem>Default Values : options = list('integertolerance',1d-06,'maxnodes',2147483647,'cputime',1d10,'allowablegap',0,'maxiter',2147483647,'gradobj',"off",'hessian',"off")</listitem>
+  // </itemizedlist>
+	// </itemizedlist>
+	//
+	// The exitflag allows to know the status of the optimization which is given back by Bonmin.
+	// <itemizedlist>
+	//   <listitem>exitflag=0 : Optimal Solution Found. </listitem>
+	//   <listitem>exitflag=1 : InFeasible Solution.</listitem>
+	//   <listitem>exitflag=2 : Output is Continuous Unbounded.</listitem>
+	//   <listitem>exitflag=3 : Limit Exceeded.</listitem>
+	//   <listitem>exitflag=4 : User Interrupt.</listitem>
+	//   <listitem>exitflag=5 : MINLP Error.</listitem>
+	// </itemizedlist>
+	//
+	// For more details on exitflag see the Bonmin page, go to http://www.coin-or.org/Bonmin
+	//
+	// Examples
+	//     //Find x in R^2 such that it minimizes the Rosenbrock function 
+	//     //f = 100*(x2 - x1^2)^2 + (1-x1)^2
+	//     //Objective function to be minimised
+	//     function y= f(x)
+	//   	    y= 100*(x(2) - x(1)^2)^2 + (1-x(1))^2;
+	//     endfunction
+	//	 //Starting point  
+	//     x0=[-1,2];
+  //     intcon = [2]
+	//     //Options
+	//     options=list("MaxIter", [1500], "CpuTime", [500]);
+	//     //Calling 
+	//     [xopt,fopt,exitflag,gradient,hessian]=intfminunc(f,x0,intcon,options)
+  // // Press ENTER to continue
+	//
+	// Examples
+	//      //Find x in R^2 such that the below function is minimum
+	//      //f = x1^2 + x2^2
+	//      //Objective function to be minimised
+	//      function y= f(x)
+	//   	     y= x(1)^2 + x(2)^2;
+	//      endfunction
+	//	  //Starting point  
+	//      x0=[2,1];
+  //      intcon = [1];  
+	//      [xopt,fopt]=intfminunc(f,x0,intcon)
+  // // Press ENTER to continue
+	//
+	// Examples
+	//     //The below problem is an unbounded problem:
+	//     //Find x in R^2 such that the below function is minimum
+	//     //f = - x1^2 - x2^2
+	//     //Objective function to be minimised
+	//     function [y,g,h] = f(x)
+	//        y = -x(1)^2 - x(2)^2;
+  //        g = [-2*x(1),-2*x(2)];
+  //        h = [-2,0;0,-2];
+	//     endfunction
+	//	 //Starting point  
+	//     x0=[2,1];
+  //     intcon = [1]
+  //     options = list("gradobj","ON","hessian","on");
+	//    [xopt,fopt,exitflag,gradient,hessian]=intfminunc(f,x0,intcon,options)
+
+	//To check the number of input and output arguments
+   	[lhs , rhs] = argn();
+	
+	//To check the number of arguments given by the user
+   	if ( rhs<2 | rhs>4 ) then
+    		errmsg = msprintf(gettext("%s: Unexpected number of input arguments : %d provided while should be int [2 3 4] "), "intfminunc", rhs);
+    		error(errmsg);
+   	end
+ 
+	//Storing the 1st and 2nd Input Parameters  
+   	fun = varargin(1);
+   	x0 = varargin(2);
+   //To add intcon
+   intcon=[];
+   if ( rhs >=3 ) then
+			intcon = varargin(3);
+	end
+
+  param = list();
+  //To check whether options has been entered by user   
+ 	if ( rhs>=4  ) then
+      param =varargin(4);
+ 	end
+  
+  nbvar = size(x0,"*");
+
+  ///////////////// To check whether the Input arguments /////////////////
+  Checktype("intfminunc", fun, "fun", 1, "function");
+  Checktype("intfminunc", x0, "x0", 2, "constant");
+  Checktype("intfminunc", intcon, "intcon", 3, "constant");
+  Checktype("intfminunc", param, "options", 4, "list");  
+  
+  ///////////////// To check x0 ///////////////// 
+  Checkvector("intfminunc", x0, "x0", 2, nbvar)
+  x0 = x0(:);
+  Checkvector("intfminbnd", intcon, "intcon", 3, size(intcon,"*"))
+  intcon = intcon(:);
+
+  //Error Checks for intcon 
+  for i=1:size(intcon,1)
+      if(intcon(i)>nbvar) then
+        errmsg = msprintf(gettext("%s: The values inside intcon should be less than the number of variables"), "intfminunc");
+        error(errmsg);
+      end
+
+      if (intcon(i)<0) then
+        errmsg = msprintf(gettext("%s: The values inside intcon should be greater than 0 "), "intfminunc");
+        error(errmsg);
+      end
+
+      if(modulo(intcon(i),1)) then
+        errmsg = msprintf(gettext("%s: The values inside intcon should be an integer "), "intfminunc");
+        error(errmsg);
+      end
+  end
+   
+  //If options has been entered, then check whether an even number of entires has been entered   
+  if (modulo(size(param),2)) then
+    errmsg = msprintf(gettext("%s: Size of parameters should be even"), "intfminunc");
+    error(errmsg);
+  end 
+   
+  intconSize = length(intcon);
+  
+  options = list('integertolerance',1d-06,'maxnodes',2147483647,'cputime',1d10,'allowablegap',0,'maxiter',2147483647,'gradobj',"off",'hessian', "off")
+
+  //Pushing param into default value
+  
+  for i = 1:(size(param))/2
+    select convstr(param(2*i-1),'l')
+      case 'integertolerance' then
+        Checktype("intfminbnd_options", param(2*i), param(2*i-1), 2*i, "constant");
+        options(2) = param(2*i);
+      case 'maxnodes' then
+        Checktype("intfminbnd_options", param(2*i), param(2*i-1), 2*i, "constant");
+        options(4) = options(2*i);
+      case 'cputime' then 
+        Checktype("intfminbnd_options", param(2*i), param(2*i-1), 2*i, "constant");
+        options(6) = options(2*i);
+      case 'allowablegap' then
+        Checktype("intfminbnd_options", param(2*i), param(2*i-1), 2*i, "constant");
+        options(8) = options(2*i);
+      case 'maxiter' then
+        Checktype("intfminbnd_options", param(2*i), param(2*i-1), 2*i, "constant");
+        options(10) = options(2*i);
+      case 'gradobj' then
+        Checktype("intfminbnd_options", param(2*i), param(2*i-1), 2*i, "string");
+        if(convstr(param(2*i),'l') == "on") then
+          options(12) = "on"
+        elseif(convstr(param(2*i),'l') == "off") then
+          options(12) = "off"
+        else
+          error(999, 'Unknown string passed in gradobj.');
+        end
+      case 'hessian' then
+        Checktype("intfminbnd_options", param(2*i), param(2*i-1), 2*i, "string");
+        if(convstr(param(2*i),'l') == "on") then
+          options(14) = "on";
+        elseif(convstr(param(2*i),'l') == "off") then
+          options(14) = "off";
+        else
+          error(999, 'Unknown string passed in hessian.');
+        end
+      else
+        error(999, 'Unknown string argument passed.');
+    end
+  end 
+
+  ///////////////// Functions Check /////////////////
+
+  //To check the match between fun (1st Parameter) and x0 (2nd Parameter)
+  if(execstr('init=fun(x0)','errcatch')==21) then
+    errmsg = msprintf(gettext("%s: Objective function and x0 did not match"), "intfminunc");
+    error(errmsg);
+  end
+
+  if(options(12) == "on") then
+    
+      if(execstr('[grad_y,grad_dy]=fun(x0)','errcatch')==59) then
+        errmsg = msprintf(gettext("%s: Gradient of objective function is not provided"), "intfminunc");
+        error(errmsg);
+      end
+
+      Checkvector("intfminunc_options", grad_dy, "dy", 12, nbvar);
+  end
+
+  if(options(14) == "on") then
+    
+      if(execstr('[hessian_y,hessian_dy,hessian]=fun(x0)','errcatch')==59) then
+        errmsg = msprintf(gettext("%s: Gradient of objective function is not provided"), "intfminunc");
+        error(errmsg);
+      end
+
+      if ( ~isequal(size(hessian),[nbvar nbvar]) ) then
+        errmsg = msprintf(gettext("%s: Size of hessian should be nbvar X nbvar"), "intfminunc");
+        error(errmsg);
+      end
+  end
+
+  //Converting the User defined Objective function into Required form (Error Detectable)
+ 	function [y,check] = _f(x)
+ 		try
+      y=fun(x)
+      [y,check] =  checkIsreal(y)
+	  catch
+		  y=0;
+		  check=1;
+	  end
+   endfunction
+    
+   //Defining an inbuilt Objective gradient function 
+    function [dy,check] = _gradf(x) 
+      if (options(12) =="on") then
+        try
+          [y,dy]=fun(x);
+          [dy,check] =  checkIsreal(dy);
+        catch
+          dy = 0;
+          check=1;
+        end
+      else
+        try 
+          dy=numderivative(fun,x);
+          [dy,check] =  checkIsreal(dy);
+        catch
+          dy=0;
+          check=1;
+        end
+      end
+    endfunction
+
+  //Defining a function to calculate Hessian if the respective user entry is OFF 
+  function [hessy,check]=_gradhess(x)
+    if (options(14) == "on") then
+      try
+        [obj,dy,hessy] = fun(x)
+        [hessy,check] =  checkIsreal(hessy)
+      catch
+        hessy = 0;
+        check=1;
+      end
+    else
+      try
+        [dy,hessy]=numderivative(fun,x)
+        [hessy,check] =  checkIsreal(hessy)
+      catch
+        hessy=0;
+        check=1;
+      end
+    end
+  endfunction
+
+    //Calling the bonmin function for solving the above problem
+	  [xopt,fopt,exitflag] = inter_fminunc(_f,_gradf,_gradhess,x0,intcon,options,intconSize,nbvar);
+
+  	//In the cases of the problem not being solved, return NULL to the output matrices
+  	if( exitflag~=0 & exitflag~=3 ) then
+  		gradient = [];
+        hessian = [];
+    else
+      [ gradient, hessian] = numderivative(_f, xopt, [], [], "blockmat");
+  	end
+
+    //To print output message
+    select exitflag
+        case 0 then
+            printf("\nOptimal Solution Found.\n");
+        case 1 then
+            printf("\nInFeasible Solution.\n");
+        case 2 then
+            printf("\nObjective Function is Continuous Unbounded.\n");
+        case 3 then
+            printf("\Limit Exceeded.\n");
+        case 4 then
+            printf("\nUser Interrupt.\n");
+        case 5 then
+            printf("\nMINLP Error.\n");
+        else
+            printf("\nInvalid status returned. Notify the Toolbox authors\n");
+            break;
+    end
+endfunction
+
+function [y, check] = checkIsreal(x)
+	if ((~isreal(x))) then
+		y = 0
+		check=1;
+	else
+        y = x;
+		check=0;
+	end	
+endfunction