Dfoptim NPM | npm.io

Derivative-free optimisation in javascript

Very simple optimisation, using the Simplex (Nelder-Mead) method or Brent's method (for single-variable functions).

We provide two interfaces. In the first, you can dfoptim a function in a single go:

const point = dfoptim.fitSimplex(target, start);

which will look for the minimum of the vector-valued function target, starting from location start.

Running the optimisation may take a while, and no information can be retrieved while it runs, so we also provide a more stateful interface. The function above can be implemented as:

const opt = new dfoptim.Simplex(target, start)
while (!opt.step()) {
  // do something
}
const point = opt.result();

Where

opt is our optimiser. At this point, it has done basic set up (creating the first simplex) but not taken any steps
The step() method advances the algorithm one step, which will take one or two evaluations of the target function and may or may not find a better point than our current best. It returns true if we have converged.
The result() method returns information about the best point.

The same pair of interfaces is provided for the Brent's method via dfoptim.fitBrent and dfoptim.Brent.