An API for Multidimensional Non-Linear Optimisation


See numexpr for a list of possible mathematical functions (some like "ceil" do not work yet). Special characters are removed. Also do not use "a/b" for division but instead "a*(b)^(-1)".

Initial Guess:

Initial search space size:

Maximal iterations:


Time limit of the algorithm : 25 seconds.




This app uses a gradient-free algorithm designed to handle "hard" optimisation problems involving "highly" non-linear functions with up to a few hundreds of parameters in an unbounded space, relying on a "smart" adaptive local search.

This simple demo should enable many interesting and possibly complex applications. A interesting use case which directly possible with this app is non-linear regression over your own data. Indeed, this can be done by using as "Formula" the explicit expression of the least-square norm of the error of your non-linear function over your data.

Since the algorithm is mostly suited for high-dimensional (N>3) functions with a gradient which is hard to compute, we acknowledge that this algorithm might not be the most efficient way to solve the minimisation of every function. This algorithm has proven very handy on many of our cases, so we can only hope it will work well for your problem as well.

A (future) extension of this app is the optimisation of functions which cannnot be writted as mathematical expressions (such as a PDE model) by using user-input values of some points of the function to optimise. If this is particularly interesting to you don't hesitate to send a mail.

We also plan to add to this interface the possibility to bound the space of parameters with conditions such as "g(x,y,z)>0". If this is particularly interesting to you don't hesitate to send a mail.

For other suggestions, bug report, if you are interested in using our algorithm for your own problem or simply want to be informed of updates, don't hesitate to send a mail as well !

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A suggestion ? A bug ? Our algorithm could be interesting for you ? Shoot us a mail and we'll do the best to answer.

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