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arXiv · 2006.04248

Learning Convex Optimization Models

Akshay Agrawal·Shane Barratt·Stephen Boyd
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Abstract

A convex optimization model predicts an output from an input by solving a convex optimization problem. The class of convex optimization models is large, and includes as special cases many well-known models like linear and logistic regression. We propose a heuristic for learning the parameters in a convex optimization model given a dataset of input-output pairs, using recently developed methods for differentiating the solution of a convex optimization problem with respect to its parameters. We describe three general classes of convex optimization models, maximum a posteriori (MAP) models, utility maximization models, and agent models, and present a numerical experiment for each.

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Akshay Agrawal, Shane Barratt, Stephen Boyd. 2020-06-07. Learning Convex Optimization Models. https://arxiv.org/abs/2006.04248

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