We have developed a multi-purpose optimization library that quickly can be deployed for solving challenging, non-linear optimization problems. All the user has to do, is to formulate the cost function (a.k.a the energy function), and specify what parameters to solve for. Our algorithm is a general purpose algorithm, which runs both in Python and in C++, or a combination thereof. It uses a robust Monte-Carlo type method that minimizes the risk of getting stuck in local minima, and is trivially parallelizable for speed.
Our method can, among other things, provide sophisticated curve fitting of data in 2 or 3 dimensions even in the absence of noise and outliers.
Besides offering advice and consulting services on how to solve your optimization problem, we can also offer software licensing, or remote cloud-based solutions via our partner and client Archethought, LLC.