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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, 2011Gabriel Haeser +2 more
exaly
Sparsity Constrained Nonlinear Optimization: Optimality Conditions and Algorithms
SIAM Journal on Optimization, 2013Amir Beck, Yonina Eldar
exaly
A New Optimality Criterion for Nonhomogeneous Markov Decision Processes
Operations Research, 1987Wallace John Hopp, James C. Bean
exaly
Reduced Optimality in Pre- and Perinatal Conditions in a Swedish Newborn Population
Neuropediatrics, 1983G Hagberg, M Kyllerman
exaly
The nonlinear bilevel programming problem:formulations,regularity and optimality conditions
Optimization, 1995M Florian
exaly