Off-the-shelf optimization functions in Matlab

xcorr: comp neuro

by Sam Derbyshire via Wikipedia – CC-BY-SA 3.0

Estimating a statistical model via maximum likelihood or MAP involves minimizing an error function – the negative log-likelihood or log-posterior. Generic functions built in to Matlab like fminunc and fmincon will often do the trick. There are many other free solvers available, which are often faster, or more powerful:

  • Solvers by Mark Schmidt: there’s a huge collection of functions from Mark Schmidt to solve generic constrained and unconstrained problems as well as solvers for more specific problems, e.g. L1-regularized problems. minFunc and minConf are drop-in replacements for fminunc and fmincon, and they are often much faster. Each function has several variants, so you can fiddle with the parameters until you get something that has acceptable speed.
  • CVX: the toolbox for convex optimization is not always the fastest, but it’s exceedingly powerful for constrained optimization. You use a declarative syntax to specify the problem and CVX takes care of finding a…

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