Canada Research Chair in Combinatorial Optimization

我们这个不断发展的社会每天都面临着大量的离散优化问题。例如，在大规模电路设计中协调车队、定位配送中心和布线。不幸的是，即使我们拥有海量的计算集群，我们仍然无法有效地准确地解决这些问题。我设计了近似算法来为这些问题找到接近最优的解决方案。这些都是有效的启发式方法，也绝对保证所找到的解在最优解的某个有界误差范围内。我的工作还在我们对实际使用的整数规划启发式的理解和计算理论之间架起了一座桥梁，可以节省时间、燃料和金钱。

Our ever-growing society is faced with a number of discrete optimization problems on a daily basis. Examples are coordinating fleets of vehicles, locating distribution centres, and routing wires in large-scale circuit design. Unfortunately, even with our massive computing clusters, we are still unable to efficiently solve these problems exactly. I devise approximation algorithms to find near-optimum solutions to these problems. These are efficient heuristics that also come with an absolute guarantee that the solutions found are within some bounded error of the optimum. My work also bridges the gap between our understanding of integer programming heuristics used in practice and the theory of computing, and can lead to savings in time, fuel, and money.*