The Nelder-Mead algorithm is a rather popular algorithm for (low dimensional) nonlinear programming. The original version (from 1965, see [1]) has known and varied failure modes, but never really had to fear for its popularity. It doesn’t need derivatives, which can be quite convenient, and has a reputation to work well even with noisy and rough functions.

Recently, a few provably convergent variants have been devised (see [2], [3], and [4]). The one by A. Bürmen et al [4], the “Grid Restrained Nelder-Mead Algorithm” (GRNMA) is notable because it does not impose a “sufficient descent” condition at each iteration, which makes it closer to the original algorithm.

A reliable, simple-to-use derivative free minimization routine is a handy little tool to have around. It turns out that implementing the GRNMA is also a pleasant programming project.

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