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 Author Comment/Response Forum Moderator email me 07/14/12 1:27pm Maximize is designed for smooth, polynomial expressions. I tried NMaximize with the same arguments and it found the same local maximum. Often a different Method works better. In[44]:= NMaximize[{int[\[Kappa]02], 0 < \[Kappa]02 < 1.5}, \[Kappa]02, Method -> "SimulatedAnnealing"] Out[44]= {0.126127, {\[Kappa]02 -> 0.203592}} In[43]:= NMaximize[{int[\[Kappa]02], 0 < \[Kappa]02 < 1.5}, \[Kappa]02, Method -> "RandomSearch"] Out[43]= {0.126127, {\[Kappa]02 -> 0.203592}} A simple way to find a maximum in a list (or good starting point for NMaximize or FindMaximum) is to find the location of largest value in the list. In[37]:= maxY = Max[ ptdata[[All, 2]] ] Out[37]= 0.126127 In[40]:= Position[ptdata, maxY] Out[40]= {{205, 2}} In[41]:= Extract[ptdata, 205] Out[41]= {0.204, 0.126127} Attachment: 25032aMaximum.nb, URL: ,

 Subject (listing for 'Maximizing solutions of NDSolve') Author Date Posted Maximizing solutions of NDSolve Stan 06/27/12 10:31am Re: Maximizing solutions of NDSolve Stan 06/29/12 08:05am Re: Re: Maximizing solutions of NDSolve Michael 06/29/12 12:31pm Re: Re: Re: Maximizing solutions of NDSolve Stan 06/30/12 12:02pm Re: Re: Re: Re: Maximizing solutions of NDSolve Michael 07/02/12 01:01am Re: Maximizing solutions of NDSolve Stan 07/09/12 07:24am Re: Re: Maximizing solutions of NDSolve Michael 07/10/12 2:30pm Re: Maximizing solutions of NDSolve Forum Modera... 07/14/12 1:27pm
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