Contrastive Losses and Solution Caching for Predict-and-Optimize

• URL: http://arxiv.org/abs/2011.05354v2
• Date: Tue, 6 Jul 2021 10:39:33 GMT
• Title: Contrastive Losses and Solution Caching for Predict-and-Optimize
• Authors: Maxime Mulamba, Jayanta Mandi, Michelangelo Diligenti, Michele Lombardi, Victor Bucarey, Tias Guns
• Abstract summary: We use a Noise Contrastive approach to motivate a family of surrogate loss functions. We address a major bottleneck of all predict-and-optimize approaches. We show that even a very slow growth rate is enough to match the quality of state-of-the-art methods.
• Score: 19.31153168397003
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many decision-making processes involve solving a combinatorial optimization problem with uncertain input that can be estimated from historic data. Recently, problems in this class have been successfully addressed via end-to-end learning approaches, which rely on solving one optimization problem for each training instance at every epoch. In this context, we provide two distinct contributions. First, we use a Noise Contrastive approach to motivate a family of surrogate loss functions, based on viewing non-optimal solutions as negative examples. Second, we address a major bottleneck of all predict-and-optimize approaches, i.e. the need to frequently recompute optimal solutions at training time. This is done via a solver-agnostic solution caching scheme, and by replacing optimization calls with a lookup in the solution cache. The method is formally based on an inner approximation of the feasible space and, combined with a cache lookup strategy, provides a controllable trade-off between training time and accuracy of the loss approximation. We empirically show that even a very slow growth rate is enough to match the quality of state-of-the-art methods, at a fraction of the computational cost.

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