Predict-Then-Optimize by Proxy: Learning Joint Models of Prediction and Optimization

TL;DR

This paper introduces a generic learning-to-optimize approach that directly models optimal solutions from features, improving efficiency and flexibility in predict-then-optimize problems compared to traditional end-to-end methods.

Contribution

It proposes a novel, adaptable framework that learns solutions directly from features, bypassing complex backpropagation through optimization, and demonstrates its effectiveness across various problems.

Findings

Learning-to-Optimize methods achieve accurate solutions

The approach is more efficient than traditional methods

Flexible across different problem types

Abstract

Many real-world decision processes are modeled by optimization problems whose defining parameters are unknown and must be inferred from observable data. The Predict-Then-Optimize framework uses machine learning models to predict unknown parameters of an optimization problem from features before solving. Recent works show that decision quality can be improved in this setting by solving and differentiating the optimization problem in the training loop, enabling end-to-end training with loss functions defined directly on the resulting decisions. However, this approach can be inefficient and requires handcrafted, problem-specific rules for backpropagation through the optimization step. This paper proposes an alternative method, in which optimal solutions are learned directly from the observable features by predictive models. The approach is generic, and based on an adaptation of the Learning-to-Optimize paradigm, from which a rich variety of existing techniques can be employed. Experimental evaluations show the ability of several Learning-to-Optimize methods to provide efficient, accurate, and flexible solutions to an array of challenging Predict-Then-Optimize problems.