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This paper proposes a novel framework for predict-then-optimize problems that characterizes expected loss as the covariance between costs and optimal decisions: E[Loss] =-cov(c, z opt (c)). This closed-form expression enables ex-ante loss estimation using simulated or historical data without requiring predictors.
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On sequential optimality conditions for smooth constrained optimization
Optimization, 2011Roberto Andreani +2 more
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Sparsity Constrained Nonlinear Optimization: Optimality Conditions and Algorithms
SIAM Journal on Optimization, 2013Amir Beck, Yonina C Eldar
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Reduced Optimality in Pre- and Perinatal Conditions in a Swedish Newborn Population
Neuropediatrics, 1983M Kyllerman, G Hagberg
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Designing Experiments with Respect to 'Standardized' Optimality Criteria
Journal of the Royal Statistical Society Series B: Statistical Methodology, 1997Holger Dette
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Local optimality of self-organising neuro-fuzzy inference systems
Information Sciences, 2019Xiaowei Gu +2 more
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Conditions for Optimality of the Huffman Algorithm
SIAM Journal on Computing, 1980openaire +1 more source