Julia is a recent high-level, high-performance, free and open-source programming language for technical computing. It has a syntax similar to other technical computing environments and possesses a number of very attractive features for the control and optimization communities: its sophisticated compiler results in run-times comparable to C, it has native support for distributed parallel execution, it includes an extensive mathematical function library, and can be executed stand-alone, in a powerful browser-based graphical notebook interface (IJulia) or in a network-based environment which does not require any local installation (JuliaBox). In addition, the Julia developer community is contributing a number of external packages through Julia’s built-in package manager at a rapid pace.

Over the last years, several Julia packages have been developed to complement the work of control engineers. These include several toolboxes for convex and nonconvex optimization, automatic differentiation, simulation of ordinary differential equations and noncausal models, statistics, machine learning, data-driven modeling and control. Many of these tools have reached a good level of maturity, so now is an appropriate time for the broader control community to learn about them, benefit from them, and eventually contribute to their further development.

This workshop is being organized for the purpose of disseminating and promoting the Julia language and several of its control and optimization-related packages to those who work in control theory, education and applications. Many of the talks will be hands-on, in order to provide the audience with a real, interactive Julia experience. Participants from industry and teachers may find Julia to be a powerful and solid alternative to commercial software. Those who do research in control and identification theory will acknowledge that the open platforms developed for these disciplines can provide a attractive means of developing and promoting their own research, by contributing to these packages with their new algorithms.