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diff --git a/code/fminsearch/FletcherPowell.sce b/code/fminsearch/FletcherPowell.sce
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+// This is an example for unconstraint nonlinear problems.
+// Ref:R.fletcher and M.J.D Powell, A Rapidly Convergent Descent Method for Minimization Algorithms, Computer journal, Vol. 6, pp. 163-168, 1963
+//Example: 
+//f(x1,x2,x3) = 100*((x3 - 10*theta(x1,x2))^2 + (sqrt(x1^2 + x1^2) - 1)^2) + x3^2
+//theta(x1,x2) = (atan(x(2)/x(1)))/(2*%pi)  if x(1)>0 
+//             = %pi + atan(x(2)/x(1))      if x(1)<0
+//======================================================================                
+// Copyright (C) 2018 - 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:Debasis Maharana
+// Organization: FOSSEE, IIT Bombay
+// Email: toolbox@scilab.in
+//======================================================================
+clc;
+
+clc;clear;close
+
+function y  = FletcherPowell(x)
+    if  (x(1)>0) 
+        theta_x1x2 = (atan(x(2)/x(1)))/(2*%pi);
+    elseif  (x(1)<0)
+        theta_x1x2 = %pi + atan(x(2)/x(1));
+    end
+    y = 100*( (x(3) - 10*theta_x1x2 ).^2 + (sqrt(x(1)^2 + x(2)^2) - 1)^2) + x(3)^2;
+endfunction
+
+X0 = [-1 0 0];
+MFes = 500;
+Miter = 200;
+TF = 1D-10;
+TX = 1D-10;
+mprintf('The following settings are used\n Maximum iterations %d \n maximum functional exaluations %d\n Function tolerance %s \n variable tolerance %s ',Miter,MFes,string(TF),string(TX));
+input('Press enter to proceed ')
+clc;
+mprintf('Scilab is solving the problem...')
+
+options = optimset ("MaxFunEvals",MFes,"MaxIter",Miter,"PlotFcns",optimplotfval,"TolFun",TF,"TolX",TX);
+
+[x,fval,exitflag,output] = fminsearch(FletcherPowell,X0,options)
+clc
+select exitflag
+case -1
+    disp(output.algorithm, 'Algorithm used')
+    mprintf('\n The maximum number of iterations has been reached \n')
+    mprintf('\n The number of iterations %d ',output.iterations)
+    mprintf('\n The number of function evaluations %d',output.funcCount)
+case 0
+    disp(output.algorithm, 'Algorithm used ')
+    mprintf('\n The maximum number of function evaluations has been reached \n') 
+    mprintf('\n The number of function evaluations %d',output.funcCount)
+    mprintf('\n The number of iterations %d ',output.iterations)
+
+case 1
+    disp(output.algorithm, 'Algorithm used ')
+    mprintf('\n The tolerance on the simplex size and function value delta has been reached\n')
+    mprintf('\n The number of function evaluations %d',output.funcCount)
+    mprintf('\n The number of iterations %d ',output.iterations)
+end
+
+disp(x,"The optimal solution is")
+mprintf("\n The optimum value of the function is %s",string(fval))