Integrated Conditional Estimation-Optimization

• 成果类型: Article
• 署名作者: Qi, Meng; Grigas, Paul; Shen, Zuo-Jun (max)
• 署名单位: Cornell University; University of California System; University of California Berkeley; University of Hong Kong; University of Hong Kong
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2023.0427
• 发表日期: 2026
• 关键词: contextual stochastic optimization; Prescriptive Analytics; statistical learning theory; integrated estimation-optimization; end-to-end learning; Combinatorial Optimization; stochastic optimization; robust

摘要

Many real-world optimization problems involve uncertain parameters with probability distributions that can be estimated using contextual feature information. In contrast to the standard approach of first estimating the distribution of uncertain parameters and then optimizing the objective based on the estimation, we propose an integrated conditional estimation-optimization (ICEO) framework that estimates the underlying conditional distribution of the random parameter while considering the structure of the optimization problem. We directly model the relationship between the conditional distribution of the random parameter and the contextual features and then estimate the probabilistic model with an objective that aligns with the downstream optimization problem. We show that our ICEO approach is asymptotically consistent under moderate regularity conditions and further provide finite performance guarantees in the form of generalization bounds. Computationally, performing estimation with the ICEO approach is a nonconvex and often nondifferentiable optimization problem. We propose a general methodology for approximating the potentially nondifferentiable mapping from estimated conditional distribution to the optimal decision by a differentiable function, which greatly improves the performance of gradient-based algorithms applied to the nonconvex problem. We also provide a polynomial optimization solution approach in the semi-algebraic case. Numerical experiments are also conducted to show the empirical success of our approach in different situations, including with limited data samples and model mismatches.