linprog general tests added

1 files changed, 105 insertions, 0 deletions

diff --git a/tests/unit_tests/fminimax.tst b/tests/unit_tests/fminimax.tst
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+// Copyright (C) 2015 - IIT Bombay - FOSSEE
+//
+// Author: Animesh Baranawal
+// Organization: FOSSEE, IIT Bombay
+// Email: toolbox@scilab.in
+//
+// 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
+
+// A case where we provide the gradient of the objective
+// functions and the Jacobian matrix of the constraints.
+// The objective function and its gradient
+function f = myfun(x)
+f(1)= 2*x(1)^2 + x(2)^2 - 48*x(1) - 40*x(2) + 304;
+f(2)= -x(1)^2 - 3*x(2)^2;
+f(3)= x(1) + 3*x(2) -18;
+f(4)= -x(1) - x(2);
+f(5)= x(1) + x(2) - 8;
+endfunction
+// Defining gradient of myfun
+function G = myfungrad(x)
+G = [ 4*x(1) - 48, -2*x(1), 1, -1, 1;
+2*x(2) - 40, -6*x(2), 3, -1, 1; ]'
+endfunction
+// The nonlinear constraints and the Jacobian
+// matrix of the constraints
+function [c,ceq] = confun(x)
+// Inequality constraints
+c = [1.5 + x(1)*x(2) - x(1) - x(2), -x(1)*x(2) - 10]
+// No nonlinear equality constraints
+ceq=[]
+endfunction
+// Defining gradient of confungrad
+function [DC,DCeq] = cgrad(x)
+// DC(:,i) = gradient of the i-th constraint
+// DC = [
+//   Dc1/Dx1  Dc1/Dx2
+//   Dc2/Dx1  Dc2/Dx2
+//   ]
+DC= [
+x(2)-1, -x(2)
+x(1)-1, -x(1)
+]'
+DCeq = []'
+endfunction
+// Test with both gradient of objective and gradient of constraints
+minimaxOptions = list("GradObj",myfungrad,"GradCon",cgrad);
+// The initial guess
+x0 = [0,10];
+// The expected solution : only 4 digits are guaranteed
+//xopt = [0.92791 7.93551]
+//fopt = [6.73443  -189.778  6.73443  -8.86342  0.86342]
+maxfopt = 6.73443
+// Run fminimax
+[xopt,fopt,maxfval,exitflag,output] = fminimax(myfun,x0,[],[],[],[],[],[], confun, minimaxOptions)
+
+assert_close ( xopt , [ 8.6737161 0.9348425 ]' , 0.0005 );
+assert_close ( fopt , [ 1.6085585 -77.855143 -6.5217563 -9.6085587 1.6085587 ]' , 0.0005 );
+assert_checkequal( exitflag , int32(0) );
+printf("Test Successful");