## Statistics & Biostatistics - Xiang Li (Queen's University)

### Wednesday, February 26th, 2020

**Time:** 11:30-12:20** Place:** Jeffery Hall 225

**Speaker:** Prof. Xiang Li (Queen's University, Dept. of Chemical Engineering)

**Title:** Decomposition Based Global Optimization

**Abstract:** Large-scale nonconvex optimization arises from a variety of scientific and engineering problems. Often such optimization problem is simplified into an easier convex or mixed-integer convex optimization problem, but the solution of the simplified problem is unlikely to be optimal or feasible for the original problem. Recent advances in decomposition based global optimization provides a promising way to solve large-scale nonconvex optimization problems within reason time. In this presentation, we will first discuss the principle of generalized Benders decomposition (GBD), including the reformulation into a master problem using strong Lagrangian duality, the construction of upper and lower bounding problems, and the finite convergence property. We also show how GBD can be applied to decompose multi-scenario problems. Then we introduce two variants of GBD. The first variant, called nonconvex generalized Benders decomposition (NGBD), is able to solve a class of nonconvex problems that GBD cannot solve. The second variant, called joint decomposition (JD), enhances GBD/NGBD via the integration of Lagrangian decomposition. Finally, we demonstrate the computational advantages of GBD, NGBD and JD via some engineering problems.