PhD Thesis

Title

Mixed-Integer Programming Approaches for some Non-convex and Combinatorial Optimization Problems.

Advisors

• Stephen J. Wright

• Jeff Linderoth

• James R. Luedtke

Preprints & Presentations

• Thesis manuscript

Abstract

In this dissertation we study several nonconvex and combinatorial optimization problems with applications in production planning, machine learning, advertising, statistics, and computer vision. The common theme is the use of algorithmic and modelling techniques from mixed-integer program- ming (MIP) which include formulation strengthening, decomposition, and linear programming (LP) rounding.