path: root/macros/qpipopt.sci

// 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
// Organization: FOSSEE, IIT Bombay
// Email: toolbox@scilab.in


function [xopt,fopt,exitflag,output,lambda] = qpipopt (varargin)
	// Solves a linear quadratic problem.
	//
	//   Calling Sequence
	//   xopt = qpipopt(nbVar,nbCon,H,f,lb,ub,A,conLB,conUB)
	//   xopt = qpipopt(nbVar,nbCon,H,f,lb,ub,A,conLB,conUB,x0)
	//   xopt = qpipopt(nbVar,nbCon,H,f,lb,ub,A,conLB,conUB,x0,param)
	//   [xopt,fopt,exitflag,output,lamda] = qpipopt( ... )
	//   
	//   Parameters
	//   nbVar : a double, number of variables
	//   nbCon : a double, number of constraints
	//   H : a symmetric matrix of double, represents coefficients of quadratic in the quadratic problem.
	//   f : a vector of double, represents coefficients of linear in the quadratic problem
	//   lb : a vector of double, contains lower bounds of the variables.
	//   ub : a vector of double, contains upper bounds of the variables.
	//   A : a matrix of double, contains the constraint matrix conLB ≤ A⋅x ≤ conUB.
	//   conLB : a vector of double, contains lower bounds of the constraints conLB ≤ A⋅x ≤ conUB. 
	//   conUB : a vector of double, contains upper bounds of the constraints conLB ≤ A⋅x ≤ conUB. 
	//   x0 : a vector of double, contains initial guess of variables.
	//   param : a list containing the parameters to be set.
	//   xopt : a vector of double, the computed solution of the optimization problem.
	//   fopt : a double, the value of the function at x.
	//   exitflag : The exit status. See below for details.
	//   output : The structure consist of statistics about the optimization. See below for details.
	//   lambda : The structure consist of the Lagrange multipliers at the solution of problem. See below for details.
	//   
	//   Description
	//   Search the minimum of a constrained linear quadratic optimization problem specified by :
	//
	//   <latex>
	//    \begin{eqnarray}
	//    &\mbox{min}_{x}
	//    & 1/2⋅x^T⋅H⋅x + f^T⋅x  \\
	//    & \text{subject to} & conLB \leq A⋅x \leq conUB \\
	//    & & lb \leq x \leq ub \\
	//    \end{eqnarray}
	//   </latex>
	//   
	//   The routine calls Ipopt for solving the quadratic problem, Ipopt is a library written in C++.
	//	
	// The exitflag allows to know the status of the optimization which is given back by Ipopt.
	// <itemizedlist>
	//   <listitem>exitflag=0 : Optimal Solution Found </listitem>
	//   <listitem>exitflag=1 : Maximum Number of Iterations Exceeded. Output may not be optimal.</listitem>
	//   <listitem>exitflag=2 : Maximum CPU Time exceeded. Output may not be optimal.</listitem>
	//   <listitem>exitflag=3 : Stop at Tiny Step.</listitem>
	//   <listitem>exitflag=4 : Solved To Acceptable Level.</listitem>
	//   <listitem>exitflag=5 : Converged to a point of local infeasibility.</listitem>
	// </itemizedlist>
	//
	// For more details on exitflag see the ipopt documentation, go to http://www.coin-or.org/Ipopt/documentation/
	//
	// The output data structure contains detailed informations about the optimization process. 
	// It has type "struct" and contains the following fields.
	// <itemizedlist>
	//   <listitem>output.iterations: The number of iterations performed during the search</listitem>
	//   <listitem>output.constrviolation: The max-norm of the constraint violation.</listitem>
	// </itemizedlist>
	//
	// The lambda data structure contains the Lagrange multipliers at the end 
	// of optimization. In the current version the values are returned only when the the solution is optimal. 
	// It has type "struct" and contains the following fields.
	// <itemizedlist>
	//   <listitem>lambda.lower: The Lagrange multipliers for the lower bound constraints.</listitem>
	//   <listitem>lambda.upper: The Lagrange multipliers for the upper bound constraints.</listitem>
	//   <listitem>lambda.eqlin: The Lagrange multipliers for the linear equality constraints.</listitem>
	//   <listitem>lambda.ineqlin: The Lagrange multipliers for the linear inequality constraints.</listitem>
	// </itemizedlist>
	//
	// Examples
	//		//Ref : example 14 :
	//		//https://www.me.utexas.edu/~jensen/ORMM/supplements/methods/nlpmethod/S2_quadratic.pdf
	//		// min. -8*x1*x1 -16*x2*x2 + x1 + 4*x2
	//		// such that
	//		//	x1 + x2 <= 5,
	//		//	x1 <= 3,
	//		//	x1 >= 0,
	//		//	x2 >= 0
	//	H = [2 0
	//		 0 8]; 
	//	f = [-8; -16];
	//  A = [1 1;1 0];
	//	conUB = [5;3];
	//	conLB = [-%inf; -%inf];
	//	lb = [0; 0];
	//	ub = [%inf; %inf];
	//	nbVar = 2;
	//	nbCon = 2;
	//	[xopt,fopt,exitflag,output,lambda] = qpipopt(nbVar,nbCon,H,f,lb,ub,A,conLB,conUB)
	//  //Press ENTER to continue
	//    
	// Examples 
	//      //Find x in R^6 such that:
	//      A= [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'⋅H⋅x + f'⋅x with
	//      f=[1; 2; 3; 4; 5; 6]; H=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,H,f,lb,ub,A,conLB,conUB,x0,param)
	// 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 = [];
	nbCon = [];
	H = [];
	f = [];
	A = [];
	conLB = [];
	conUB = [];
	lb = [];
	ub = [];
   
	nbVar = varargin(1);
	nbCon = varargin(2);
	H = varargin(3);
	f = varargin(4);
	lb = varargin(5);
	ub = varargin(6);
	A = varargin(7);
	conLB = varargin(8);
	conUB = varargin(9);

    if (size(lb,2)==0) then
        lb = repmat(-%inf,nbVar,1);
    end
    
    if (size(ub,2)==0) then
        ub = repmat(%inf,nbVar,1);
    end

    if (size(f,2)==0) then
        f = repmat(0,nbVar,1);
    end
    
	if ( rhs<10 | size(varargin(10)) ==0 ) then
		x0 = repmat(0,nbVar,1);
	else
		x0 = varargin(10);
	end
   
   if ( rhs<11 | size(varargin(11)) ==0 ) then
      param = list(); 
   else
      param =varargin(11);
   end
   
   if (type(param) ~= 15) then
      errmsg = msprintf(gettext("%s: param should be a list "), "qpipopt");
      error(errmsg);
   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(2*i) = param(2*i);
       	case "CpuTime" then
          		options(2*i) = param(2*i);
    	else
    	      errmsg = msprintf(gettext("%s: Unrecognized parameter name ''%s''."), "qpipopt", param(2*i-1));
    	      error(errmsg)
    	end
   end

// Check if the user gives row vector 
// and Changing it to a column matrix

	if (size(f,2)== [nbVar]) then
		f=f';
	end

	if (size(lb,2)== [nbVar]) then
		lb = lb';
	end

   if (size(ub,2)== [nbVar]) then
      ub = ub';
   end

   if (size(conUB,2)== [nbCon]) then
      conUB = conUB';
   end

   if (size(conLB,2)== [nbCon]) then
      conLB = conLB';
   end

   if (size(x0,2)== [nbVar]) then
	x0=x0';
   end

   //IPOpt wants it in row matrix form
   f = f';
   lb = lb';
   ub = ub';
   conLB = conLB';
   conUB = conUB';
   x0 = x0';
   
   //Checking the H matrix which needs to be a symmetric matrix
   if ( ~isequal(H,H') ) then
      errmsg = msprintf(gettext("%s: H is not a symmetric matrix"), "qpipopt");
      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"), "qpipopt");
      error(errmsg);
   end
   
   //Check the size of p which should equal to the number of variable
   if ( size(f,2) ~= [nbVar]) then
      errmsg = msprintf(gettext("%s: The Size of f is not equal to the number of variables"), "qpipopt");
      error(errmsg);
   end
   
   if (nbCon) then
          //Check the size of constraint which should equal to the number of variables
       if ( size(A,2) ~= nbVar) then
          errmsg = msprintf(gettext("%s: The size of constraints is not equal to the number of variables"), "qpipopt");
          error(errmsg);
       end
   end

   //Check the number of constraint
   if ( size(A,1) ~= nbCon) then
      errmsg = msprintf(gettext("%s: The size of constraint matrix is not equal to the number of constraint given i.e. %d"), "qpipopt", nbCon);
      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 size of 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 size of 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 size of 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 size of 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 | size(x0,"*")>nbVar) then
      warnmsg = msprintf(gettext("%s: Ignoring initial guess of variables as it is not equal to the number of variables"), "qpipopt");
      warning(warnmsg);
	  x0 = repmat(0,1,nbVar);
   end
   
   //Check if the user gives a matrix instead of a vector
   
   if ((size(f,1)~=1)& (size(f,2)~=1)) then
      errmsg = msprintf(gettext("%s: f should be a vector"), "qpipopt");
      error(errmsg); 
   end
   
   if (size(lb,1)~=1)& (size(lb,2)~=1) then
      errmsg = msprintf(gettext("%s: Lower Bound should be a vector"), "qpipopt");
      error(errmsg); 
   end
   
   if (size(ub,1)~=1)& (size(ub,2)~=1) then
      errmsg = msprintf(gettext("%s: Upper Bound should be a vector"), "qpipopt");
      error(errmsg); 
   end
   
   if (nbCon) then
        if ((size(conLB,1)~=1)& (size(b,2)~=1)) then
            errmsg = msprintf(gettext("%s: Constraint Lower Bound should be a vector"), "qpipopt");
            error(errmsg); 
        end

        if (size(conUB,1)~=1)& (size(beq,2)~=1) then
            errmsg = msprintf(gettext("%s: Constraint should be a vector"), "qpipopt");
            error(errmsg); 
        end
   end
   
    // Check if the user gives infinity or negative infinity in conLB or conUB
	for i = 1:nbCon
		if (conLB(i) == %inf)
		   	errmsg = msprintf(gettext("%s: Value of Lower Bound can not be infinity"), "qpipopt");
    		error(errmsg); 
  		end	

		if (conUB(i) == -%inf)
		   	errmsg = msprintf(gettext("%s: Value of Upper Bound can not be negative infinity"), "qpipopt");
    		error(errmsg); 
		end	
	end

	for i = 1:nbVar
		if(lb(i)>ub(i))
			errmsg = msprintf(gettext("%s: Problem has inconsistent variable bounds"), "lsqlin");
			error(errmsg);
		end
	end

	[xopt,fopt,status,iter,Zl,Zu,lmbda] = solveqp(nbVar,nbCon,H,f,A,conLB,conUB,lb,ub,x0,options);
   
   xopt = xopt';
   exitflag = status;
	output = struct("Iterations"      , [], ..
					"ConstrViolation" ,[]);
	output.Iterations = iter;
	output.ConstrViolation = max([0;(conLB'-A*xopt);(A*xopt - conUB');(lb'-xopt);(xopt-ub')]);
   lambda = struct("lower"           , [], ..
                   "upper"           , [], ..
                   "constraint"      , []);
   
   lambda.lower = Zl;
   lambda.upper = Zu;
   lambda.constraint = lmbda;

    select status
    case 0 then
        printf("\nOptimal Solution Found.\n");
    case 1 then
        printf("\nMaximum Number of Iterations Exceeded. Output may not be optimal.\n");
    case 2 then
        printf("\nMaximum CPU Time exceeded. Output may not be optimal.\n");
    case 3 then
        printf("\nStop at Tiny Step\n");
    case 4 then
        printf("\nSolved To Acceptable Level\n");
    case 5 then
        printf("\nConverged to a point of local infeasibility.\n");
    case 6 then
        printf("\nStopping optimization at current point as requested by user.\n");
    case 7 then
        printf("\nFeasible point for square problem found.\n");
    case 8 then 
        printf("\nIterates diverging; problem might be unbounded.\n");
    case 9 then
        printf("\nRestoration Failed!\n");
    case 10 then
        printf("\nError in step computation (regularization becomes too large?)!\n");
    case 12 then
        printf("\nProblem has too few degrees of freedom.\n");
    case 13 then
        printf("\nInvalid option thrown back by Ipopt\n");
    case 14 then
        printf("\nNot enough memory.\n");
    case 15 then
        printf("\nINTERNAL ERROR: Unknown SolverReturn value - Notify Ipopt Authors.\n");
    else
        printf("\nInvalid status returned. Notify the Toolbox authors\n");
        break;
    end
    
endfunction