Campuses:

Integer programming

Monday, August 21, 2017 - 2:45pm - 3:30pm
Ted Ralphs (Lehigh University)
In this talk, we describe a range of tools available for modeling and solving mathematical optimization problems in Python. The tools range from high-level Python-based modeling languages to low-level solver APIs for passing optimization problem data directly to solvers. Examples and code snippets will be given to illustrate the use of these tools. All tools described are available open source in the Computational Infrastructure for Operations Research (COIN-OR) repository.
Wednesday, August 10, 2016 - 2:00pm - 3:30pm
A landmark result on the complexity of integer programming by Lenstra in 1983 harnesses lattice basis reduction techniques to prove that, in constant dimension, integer linear programming can be solved in polynomial time. This algorithm also provides a fixed parameter tractable algorithm for mixed integer linear programming. We will cover Lenstra's algorithm from a modern perspective using Khinchine's flatness theorem, lattice theory of closest vector problem and shortest vector problem, and ellipsoid rounding.
Monday, February 23, 2015 - 1:30pm - 2:30pm
Natashia Boland (Georgia Institute of Technology)
The need to schedule activities lies at the heart of many supply chain and logistics operations. Effective scheduling has long been critical to profitability. More recently, the impetus towards tighter delivery timeframes as an essential aspect of a logistics service has been growing strongly, as internet business drives increased consumer expectations of delivery services, and large online retailers see shorter delivery times as a serious competitive edge. But scheduling has long been a challenge for optimization.
Wednesday, November 19, 2008 - 11:15am - 12:00pm
Uday Shanbhag (University of Illinois at Urbana-Champaign)
Joint work with Walter Murray (Stanford University).

The siting and sizing of electrical substations on a rectangular electrical grid can be formulated
as an integer programming problem with a quadratic objective and linear constraints. We propose a novel
approach that is based on solving a sequence of local relaxations of the problem for a given number of
substations. Two methods are discussed for determining a new location from the solution of the relaxed
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