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Diffstat (limited to 'tests/unit_tests/fminimax.dia.ref')
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diff --git a/tests/unit_tests/fminimax.dia.ref b/tests/unit_tests/fminimax.dia.ref new file mode 100644 index 0000000..e887a38 --- /dev/null +++ b/tests/unit_tests/fminimax.dia.ref @@ -0,0 +1,105 @@ +// 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"); |