TL;DR
The paper introduces the SPO framework, a novel approach that directly incorporates optimization problem structure into prediction models, leading to improved decision-making in predict-then-optimize tasks.
Contribution
It proposes the SPO loss and SPO+ surrogate loss, enabling prediction models to be trained specifically for optimization performance, with proven statistical consistency.
Findings
SPO framework improves decision quality in shortest path and portfolio problems.
Linear models trained with SPO+ outperform random forests in certain scenarios.
The SPO+ loss is convex and handles complex optimization problems efficiently.
Abstract
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 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 the downstream optimization problem. In contrast, we propose a new and very general framework, called Smart "Predict, then Optimize" (SPO), which directly leverages the optimization problem structure, i.e., its objective and constraints, for designing better prediction models. A key component of our framework is the SPO loss function which measures the decision error induced by a prediction. Training a prediction model with respect to the SPO loss is computationally challenging, and thus we derive, using duality theory, a convex surrogate loss…
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