dfoptim

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.

Example

Run

`
npm run build
npm run webpack
`

Then open example/index.html` for a simple example.

Licence

MIT © Imperial College of Science, Technology and Medicine

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.