Before starting with tutorials we suggest you to read documentation#getstarted which will provide an example of data communication between your client code and Indie Solver.

While our API is programming language-agnostic, all tutorials given here have language-specific versions, currently for Python, C++, MATLAB, Java, C#, NodeJS and R.

If you would like some case to be covered in a tutorial or if your preferred programming language/platform is not yet supported, let us know at contact@indiesolver.com.

Tutorial name Description & link
Introduction tutorial: optimization in mixed domains of continuous, discrete and categorical parameters This tutorial is the best starting point to get familiar with Indie Solver. It covers problem formulation in your client code and results analysis in your dashboard.
Introduction tutorial: single-objective optimization of the classic Rosenbrock problem This tutorial is the place to start if you are interested in continuous/real-valued optimization.
Advanced tutorial: multifidelity optimization on toy surrogates of Deep Neural Networks This tutorial focuses on multifidelity optimization where you can access, evaluate and benefit from cheaper low-fidelity objective function evaluations to obtain high-quality solutions at a fraction of cost which would be required with high-fidelity-only evaluations. The title of this tutorial mentions DNNs (Deep Neural Networks) because we refer to optimization of DNNs for illustration purposes. Otherwise, the same material can be applied to other domains.