Machine learning for combinatorial optimization: A methodological tour d’horizon

Statistical Machine Learning and Combinatorial Optimization

Large scale combinatorial optimization: A methodological viewpoint

In this article, the author describes the results of a collaborative European project work with Abstract. The industrial and commercial worlds are increasingly competitive , requiring companiesto be more productiveand more responsiveto market changes (e.g. globalisation and privatisation). As a consequence, there is a strong need for solutions to large scale optimization problems, in domains su...

A Reinforcement Learning Framework for Combinatorial Optimization

The combination of reinforcement learning methods with neural networks has found success on a growing number of large-scale applications, including backgammon move selection (Tesauro 1992), elevator control (Crites & Barto 1996), and job-shop scheduling (Zhang & Dietterich 1995). In this work, we modify and generalize the scheduling paradigm used by Zhang and Dietterich to produce a general rei...

Combinatorial Optimization on a Boltzmann Machine

We discuss the problem of solving (approximately) combinatorial optimization problems on a Boltzmann machine. It is shown for a number of combinatorial optimization problems how they can be mapped directly onto a Boltzmann machine by choosing appropriate connection patterns and connection strengths. In this way maximizing the consensus in the Boltzmann machine is equivalent to finding an optima...

Optimization for Machine Learning

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