Talk Title

Parallel Preprocessing for Mixed-Integer Linear Programming

Abstract

Parallel compute can be leveraged to tremendous effect in certain areas of optimization—enabling, for instance, large language models. However, in other areas the state-of-the-art optimization algorithms are unable to make substantial use of parallelism. As a result, there is a growing divide between hardware and software advances for many major optimization problems. This talk presents two recent projects that address some aspects of this divide. The first project considers the problem of projecting a vector onto a (possibly weighted) simplex; such projection is a central subroutine for various more complex problems. The second involves conflict cut generation and management for (generic) mixed-integer linear programming problems. Both projects share an algorithmic design focus on preprocessing, where parallel compute is applied beforehand in order to accelerate a serial main method that is subsequently run. We will consider both theoretical and practical aspects of this approach in contrast to other possibilities.

Brief Bio

Chen Chen is an Assistant Professor in the Integrated Systems Engineering department at The Ohio State University, as well as a core faculty member of its Sustainability Institute. He received a PhD in Industrial Engineering and Operations Research from UC Berkeley in 2015, and was thereafter a postdoctoral researcher in the Industrial Engineering & Operations Research department at Columbia University until 2017. He is interested in various aspects of global optimization, including: mixed-integer conic optimization; nonconvex continuous problems such as polynomial or signomial optimization; interplay with machine learning; and hardware acceleration. Such work is motivated by a variety of applications, especially pertaining to power systems.