**October 1 – 31, 2019**

**Organizers: Andrea Lodi (Polytechnique Montréal), Bruce Shepherd (McGill)**

Mixed integer nonlinear programming (MINLP) is concerned with finding optimal solutions to mathematical optimization models that combine both discrete and nonlinear elements. Models with this flavor are arising in important applications in many domains, notably chemical engineering, energy, and transportation. Moreover, the well-developed frameworks for discrete and continuous optimization are not sufficient in themselves to attack this new class of problems. The underlying mathematical complexity is not well understood due to the interaction of non-convexities arising from both the discrete and nonlinear components. In particular, there remain theoretical, algorithmic and computational challenges before MINLP can enjoy a success similar to, say, smooth optimization or integer programming. These challenges are at the core of the activities of the “Mixed Integer Nonlinear Programming: Theory and Computation” thematic month at the CRM.