AF: Small: Approximation Techniques for Combinatorial Optimization

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Approximation techniques are valuable in the design of algorithms for combinatorial optimization problems. Mathematical programming relaxations provide tractable versions of hard optimization problems that are useful in the design of good algorithms -- in some cases, these relaxations and their duals serve as a guide for the design of algorithms and lower bound proofs. Such approximation techniques are useful not just in traditional optimization problems, but also for problems in online algorithms and other areas. This project proposes to study a variety of problems where approximation techniques play a crucial role -- many of these questions are about basic problems in approximation algorithms, but many are about questions where insights from mathematical relaxations and other approximation techniques play an important role. The broad goals of this project include (a) Obtaining a better understanding of the use of lift-and-project relaxations for optimization problems like coloring and the closely related question of developing algorithmic techniques for graphs of low threshold rank, (b) Attempting to close gaps in our understanding of classical optimization problems like the traveling salesman problem and bin packing, and (c) Shedding light on newer problems like online versions of weighted matching and new flow formulations.Optimization problems are ubiquitous and for many such problems of interest, we have strong evidence that it is impossible to obtain exact efficient solutions. To circumvent this intractability, we design efficient heuristics that may not find the best solution necessarily, but have guarantees that the solution they produce is not far from the optimal (i.e. is approximately optimal). Mathematical programming is a very important tool in designing such approximation algorithms. Successfully achieving the project goals will require advances in our knowledge of this area, and especially new insights into the powerful and versatile mathematical programming toolkit. As part of this project, graduate and undergraduate students will be trained by involving them in these research activities. Course materials for graduate and undergraduate courses will be developed distilling research results of this project, as well as new developments in the field.