Le 16/08/2013 18:55, Ajo Fod a écrit : > The algorithm computes the Hessian using an update rule. My question was > what if you can compute the hessian analytically? > > Hessian: http://en.wikipedia.org/wiki/Hessian_matrix > Gradient: http://en.wikipedia.org/wiki/Gradient
We do have support to help generating second derivatives in the analysis package, but don't use them yet in the optimizers. best regards, Luc > > Cheers, > -Ajo > > > On Fri, Aug 16, 2013 at 9:39 AM, Luc Maisonobe <luc.maison...@free.fr>wrote: > >> Le 15/08/2013 22:59, Ajo Fod a écrit : >>> Hello, >>> >>> Is'nt there an advantage to being able to compute the Jacobian of the >>> gradient precisely at a point? >>> >>> If so, is there a class that uses the Jacobian instead of estimating the >>> jacobian from the last few iteration as >> NonLinearConjugateGradientOptimizer >>> does? >> >> I'm not sure what you really mean, but you can always pass an >> ObjectiveFunctionGradient holding any MultivariateVectorFunction to be >> used by the algorithm. >> >> Luc >> >>> >>> Thanks, >>> -Ajo >>> >> >> >> --------------------------------------------------------------------- >> To unsubscribe, e-mail: dev-unsubscr...@commons.apache.org >> For additional commands, e-mail: dev-h...@commons.apache.org >> >> > --------------------------------------------------------------------- To unsubscribe, e-mail: dev-unsubscr...@commons.apache.org For additional commands, e-mail: dev-h...@commons.apache.org
