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arXiv Machine Learning By Shivaram Subramanian, Zhengliang Xue, Markus Ettl, Yingdong Lu, Jayant Kalagnanam

Constrained Triple-Head Price Optimization (C3PO) Network for Discrete-Choice Models

Read the original on arXiv Machine Learning →

The paper introduces the Constrained Triple-Head Price Optimization (C3PO) network, a transformer-based model designed for real‑time bi‑level decision making in discrete‑choice settings. C3PO integrates price imitation learning, multi‑task revenue prediction, and in‑context elasticity learning, while a lightweight differentiable constraint layer enforces business pricing rules. Experiments on simulated, synthetic, and real‑world datasets show that C3PO outperforms calibrated MNL baselines by up to 7.9 percentage points in expected revenue and reduces constraint violations by over 50%, with significant improvements in healthcare tender and airline ancillary pricing applications.

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