Combinatorial Optimization

Description

Combinatorial optimization involves finding the best (e.g. cheapest, most valuable, smallest, biggest) among a huge but finite set of candidates. Examples include job-shop scheduling and the traveling salesman problem. Research at Brown focuses on three areas:

Solution methods for traditional optimization problems, including methods for finding exact solutions (e.g. constraint and integer programming, dynamic programming),
approximation algorithms that are guaranteed to find solutions nearly as good as the best, and heuristic methods that find good solutions for most but not all possible instances.
Stochastic and on-line optimization problems in which the data are uncertain and/or not known a priori.
Modeling tools that streamline the development of solution methods.