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
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.
minConfare drop-in replacements for
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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