Dear Milind, It's often helpful to try (where possible):
1. Generate some synthetic data and check if the minimum you find corresponds to the 'true' solution. You could manually evaluate the objective function at the 'true' solution and a few nearby points in order to verify that it is indeed a minimum. 2. A very simple case where you can plot the objective function and verify that the minimum is being located correctly. Kind Regards, James -- James Barrett Postdoctoral Research Student Institute of Mathematical and Molecular Biomedicine King's College London On 24 Jun 2013, at 22:26, Steven G. Johnson <[email protected]> wrote: > > On Jun 24, 2013, at 3:57 PM, milind d <[email protected]> wrote: >> I have just newly started to use nlopt, I have written a program which >> uses the nlopt to minize a function, My doubt here was I am getting the >> functional value same at each optimization cycle, I knw this as i print the >> functional value inside myfunction subroutine, i m using a gradient based >> optimization algorithms, SLSQP, I have written a subroutine which calculate >> the gradient of the function n i use the gradient subroutine in myfunction. >> My data set is so huge that its not actully feasible to validate my gradient >> with tht data set, allthough i have validated it by small data sets. So my >> question is what are the possibles things which will make my functional >> values constant for many optimization cycles. > > I would try validating your gradient just by comparing with finite-difference > calculations. > > > > _______________________________________________ > NLopt-discuss mailing list > [email protected] > http://ab-initio.mit.edu/cgi-bin/mailman/listinfo/nlopt-discuss >
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