Smart Predict-and-Optimize for Hard Combinatorial Optimization Problems

Smart "Predict, then Optimize"

Many real-world analytics problems involve two significant challenges: prediction and optimization. Due to the typically complex nature of each challenge, the standard paradigm is to predict, then optimize. By and large, machine learning tools are intended to minimize prediction error and do not account for how the predictions will be used in a downstream optimization problem. In contrast, we p...

Towards Hybrid Methods for Solving Hard Combinatorial Optimization Problems

Combinatorial Optimization is a branch of optimization in applied mathematics and computer science, related to operations research, algorithm theory and computational complexity theory that sits at the intersection of many fields, such as artificial intelligence,mathematics and software engineering. Combinatorial optimization problems commonly imply finding values to a set of variables which ar...

Heuristics from Nature for Hard Combinatorial Optimization Problems

In this paper we try to describe the main characters of Heuristics \derived" from Nature, a border area between Operations Research and Arti cial Intelligence, with applications to graph optimization problems. These algorithms take inspiration from physics, biology, social sciences, and use a certain amount of repeated trials, given by one or more \agents" operating with a mechanism of competit...

Workshop on Tractable special cases of hard combinatorial optimization problems

Tractable special cases through linear algebra Gerhard Woeginger, Eindhoven University of Technology, The Netherlands The talk surveys and discusses a number of tractable special cases of the travelling salesman problem and the quadratic assignment problem. As a common theme, the underlying cost matrices can be defined and easily understood through basic linear algebra: they form a cone, or a l...

Construction heuristics for hard combinatorial optimisation problems