On the Impossibility of Statistically Improving Empirical Optimization: A Second Order Stochastic Dominance Perspective

Published Online:https://doi.org/10.1287/mnsc.2024.04482

References

  • Asmussen S, Glynn PW (2007) Stochastic Simulation: Algorithms and Analysis, vol. 57 (Springer Science & Business Media, New York), 10.CrossrefGoogle Scholar
  • Ban GY, Rudin C (2019) The big data newsvendor: Practical insights from machine learning. Oper. Res. 67(1):90–108.LinkGoogle Scholar
  • Bayraksan G, Love DK (2015) Data-driven stochastic programming using phi-divergences. INFORMS TutORials in Operations Research (INFORMS, Cantonsville, MD), 1–19.LinkGoogle Scholar
  • Ben-Tal A, El Ghaoui L, Nemirovski A (2009) Robust Optimization (Princeton University Press, Princeton, NJ).CrossrefGoogle Scholar
  • Ben-Tal A, Den Hertog D, De Waegenaere A, Melenberg B, Rennen G (2013) Robust solutions of optimization problems affected by uncertain probabilities. Management Sci. 59(2):341–357.LinkGoogle Scholar
  • Bertsimas D, Brown DB, Caramanis C (2011) Theory and applications of robust optimization. SIAM Rev. 53(3):464–501.CrossrefGoogle Scholar
  • Bertsimas D, Gupta V, Kallus N (2018) Robust sample average approximation. Math. Programming 171(1–2):217–282.CrossrefGoogle Scholar
  • Besbes O, Mouchtaki O (2023) How big should your data really be? Data-driven newsvendor: Learning one sample at a time. Management Sci. 69(10):5848–5865.Google Scholar
  • Birge JR, Louveaux F (2011) Introduction to Stochastic Programming (Springer Science & Business Media, New York).CrossrefGoogle Scholar
  • Blanchet J, Murthy K (2019) Quantifying distributional model risk via optimal transport. Math. Oper. Res. 44(2):565–600.LinkGoogle Scholar
  • Blanchet J, He F, Murthy K (2020) On distributionally robust extreme value analysis. Extremes 23(2):317–347.CrossrefGoogle Scholar
  • Blanchet J, Kang Y, Murthy K (2019) Robust Wasserstein profile inference and applications to machine learning. J. Appl. Probab. 56(3):830–857.CrossrefGoogle Scholar
  • Chen R, Paschalidis IC (2018) A robust learning approach for regression models based on distributionally robust optimization. J. Machine Learn. Res. 19(13):1–48. Google Scholar
  • Chen X, He S, Jiang B, Ryan CT, Zhang T (2021) The discrete moment problem with nonconvex shape constraints. Oper. Res. 69(1):279–296.LinkGoogle Scholar
  • Chen L, Ma W, Natarajan K, Simchi-Levi D, Yan Z (2022) Distributionally robust linear and discrete optimization with marginals. Distributionally robust linear and discrete optimization with marginals. Oper. Res. 70(3):1822–1834.Google Scholar
  • Chu LY, Shanthikumar JG, Shen ZJM (2008) Solving operational statistics via a Bayesian analysis. Oper. Res. Lett. 36(1):110–116.CrossrefGoogle Scholar
  • Delage E, Ye Y (2010) Distributionally robust optimization under moment uncertainty with application to data-driven problems. Oper. Res. 58(3):595–612.LinkGoogle Scholar
  • Dhara A, Das B, Natarajan K (2021) Worst-case expected shortfall with univariate and bivariate marginals. INFORMS J. Comput. 33(1):370–389.LinkGoogle Scholar
  • Doan XV, Li X, Natarajan K (2015) Robustness to dependency in portfolio optimization using overlapping marginals. Oper. Res. 63(6):1468–1488.LinkGoogle Scholar
  • Donti P, Amos B, Kolter JZ (2017) Task-based end-to-end model learning in stochastic optimization. Adv. Neural Inform. Processing Systems (Long Beach, CA), vol. 30. Google Scholar
  • Duchi JC, Namkoong H (2019) Variance-based regularization with convex objectives. J. Machine Learn. Res. 20:68–61.Google Scholar
  • Duchi JC, Ruan F (2021) Asymptotic optimality in stochastic optimization. Ann. Statist. 49(1):21–48.CrossrefGoogle Scholar
  • Duchi JC, Glynn PW, Namkoong H (2021) Statistics of robust optimization: A generalized empirical likelihood approach. Math. Oper. Res. 46(3):946–969.LinkGoogle Scholar
  • Dupuis P, Katsoulakis MA, Pantazis Y, Plechác P (2016) Path-space information bounds for uncertainty quantification and sensitivity analysis of stochastic dynamics. SIAM/ASA J. Uncertainty Quant. 4(1):80–111.CrossrefGoogle Scholar
  • El Ghaoui L, Oks M, Oustry F (2003) Worst-case value-at-risk and robust portfolio optimization: A conic programming approach. Oper. Res. 51(4):543–556.LinkGoogle Scholar
  • Elmachtoub AN, Grigas P (2022) Smart “predict, then optimize.” Management Sci. 68(1):9–26.LinkGoogle Scholar
  • Elmachtoub AN, Lam H, Zhang H, Zhao Y (2023) Estimate-then-optimize versus integrated-estimation-optimization: A stochastic dominance perspective. Preprint, submitted April 13, https://arxiv.org/abs/2304.06833.Google Scholar
  • Esfahani PM, Kuhn D (2018) Data-driven distributionally robust optimization using the Wasserstein metric: Performance guarantees and tractable reformulations. Math. Programming 171(1–2):115–166.CrossrefGoogle Scholar
  • Fan W, Hong LJ, Zhang X (2020) Distributionally robust selection of the best. Management Sci. 66(1):190–208.LinkGoogle Scholar
  • Feng Q, Shanthikumar JG (2023) The framework of parametric and nonparametric operational data analytics. Production Oper. Management 32(9):2685–2703.CrossrefGoogle Scholar
  • Friedman J, Hastie T, Tibshirani R (2001) The Elements of Statistical Learning, vol. 1 (Springer Series in Statistics, New York).Google Scholar
  • Gao R, Kleywegt A (2023) Distributionally robust stochastic optimization with Wasserstein distance. Math. Oper. Res. 48(2):603–655.LinkGoogle Scholar
  • Gao R, Chen X, Kleywegt AJ (2024) Wasserstein distributionally robust optimization and variation regularization. Oper. Res. 72(3):1177–1191.LinkGoogle Scholar
  • Ghosh S, Lam H (2019) Robust analysis in stochastic simulation: Computation and performance guarantees. Oper. Res. 67(1):232–249.LinkGoogle Scholar
  • Glasserman P, Xu X (2014) Robust risk measurement and model risk. Quant. Finance 14(1):29–58.CrossrefGoogle Scholar
  • Goh J, Sim M (2010) Distributionally robust optimization and its tractable approximations. Oper. Res. 58(4):902–917.LinkGoogle Scholar
  • Gotoh JY, Kim MJ, Lim AE (2018) Robust empirical optimization is almost the same as mean–variance optimization. Oper. Res. Lett. 46(4):448–452.CrossrefGoogle Scholar
  • Gotoh JY, Kim MJ, Lim AE (2021) Calibration of distributionally robust empirical optimization models. Oper. Res. 69(5):1630–1650.LinkGoogle Scholar
  • Gupta V (2019) Near-optimal Bayesian ambiguity sets for distributionally robust optimization. Management Sci. 65(9):4242–4260.LinkGoogle Scholar
  • Gupta V, Kallus N (2021) Data pooling in stochastic optimization. Management Sci. 68(3):1595–1615.Google Scholar
  • Gupta V, Rusmevichientong P (2021) Small-data, large-scale linear optimization with uncertain objectives. Management Sci. 67(1):220–241.LinkGoogle Scholar
  • Hadar J, Russell WR (1969) Rules for ordering uncertain prospects. Amer. Econom. Rev. 59(1):25–34.Google Scholar
  • Hampel FR (1974) The influence curve and its role in robust estimation. J. Amer. Statist. Assoc. 69(346):383–393.CrossrefGoogle Scholar
  • Hanasusanto GA, Roitch V, Kuhn D, Wiesemann W (2015) A distributionally robust perspective on uncertainty quantification and chance constrained programming. Math. Programming 151(1):35–62.CrossrefGoogle Scholar
  • Hanoch G, Levy H (1969) The efficiency analysis of choices involving risk. Rev. Econom. Stud. 36(3):335–346.CrossrefGoogle Scholar
  • Hansen LP, Sargent TJ (2008) Robustness (Princeton University Press, Princeton, NJ).CrossrefGoogle Scholar
  • Higle JL, Sen S (1996) Stochastic Decomposition: A Statistical Method for Large Scale Stochastic Linear Programming, vol. 8 (Springer Science & Business Media, Dordrecht, Netherlands).CrossrefGoogle Scholar
  • Hong LJ, Huang Z, Lam H (2020) Learning-based robust optimization: Procedures and statistical guarantees. Management Sci. 67(6):3447–3467.Google Scholar
  • Hu Y, Kallus N, Mao X (2022) Fast rates for contextual linear optimization. Management Sci. 68(6):4236–4245.LinkGoogle Scholar
  • Iyengar G (2005) Robust dynamic programming. Math. Oper. Res. 30(2):257–280.LinkGoogle Scholar
  • Iyengar G, Lam H, Wang T (2023) Hedging against complexity: Distributionally robust optimization with parametric approximation. Internat. Conf. Artificial Intelligence Statist. (PMLR, New York), 9976–10011.Google Scholar
  • Jiang R, Guan Y (2016) Data-driven chance constrained stochastic program. Math. Programming 158(1):291–327.CrossrefGoogle Scholar
  • Kosorok MR (2007) Introduction to Empirical Processes and Semiparametric Inference (Springer Science & Business Media, New York).Google Scholar
  • Kuhn D, Esfahani PM, Nguyen VA, Shafieezadeh-Abadeh S (2019) Wasserstein distributionally robust optimization: Theory and applications in machine learning. INFORMS TutORials in Operations Research (INFORMS, Cantonsville, MD), 130–166.LinkGoogle Scholar
  • Lam H (2016) Robust sensitivity analysis for stochastic systems. Math. Oper. Res. 41(4):1248–1275.LinkGoogle Scholar
  • Lam H (2018) Sensitivity to serial dependency of input processes: A robust approach. Management Sci. 64(3):1311–1327.LinkGoogle Scholar
  • Lam H (2019) Recovering best statistical guarantees via the empirical divergence-based distributionally robust optimization. Oper. Res. 67(4):1090–1105.AbstractGoogle Scholar
  • Lam H, Mottet C (2017) Tail analysis without parametric models: A worst-case perspective. Oper. Res. 65(6):1696–1711.LinkGoogle Scholar
  • Lam H, Zhou E (2017) The empirical likelihood approach to quantifying uncertainty in sample average approximation. Oper. Res. Lett. 45(4):301–307.CrossrefGoogle Scholar
  • Landsberger M, Meilijson I (1993) Mean-preserving portfolio dominance. Rev. Econom. Stud. 60(2):479–485.CrossrefGoogle Scholar
  • Li KC (1986) Asymptotic optimality of CL and generalized cross-validation in ridge regression with application to spline smoothing. Ann. Statist. 14(3):1101–1112.CrossrefGoogle Scholar
  • Li KC (1987) Asymptotic optimality for Cp, CL, cross-validation and generalized cross-validation: Discrete index set. Ann. Statist. 15(3):958–975. CrossrefGoogle Scholar
  • Li B, Jiang R, Mathieu JL (2019) Ambiguous risk constraints with moment and unimodality information. Math. Programming 173:151–192.CrossrefGoogle Scholar
  • Lim AE, Shanthikumar JG, Shen ZM (2006) Model uncertainty, robust optimization, and learning. INFORMS TutORials in Operations Research (INFORMS, Cantonsville, MD), 66–94.LinkGoogle Scholar
  • Liyanage LH, Shanthikumar JG (2005) A practical inventory control policy using operational statistics. Oper. Res. Lett. 33(4):341–348.CrossrefGoogle Scholar
  • Petersen IR, James MR, Dupuis P (2000) Minimax optimal control of stochastic uncertain systems with relative entropy constraints. IEEE Trans. Automatic Control 45(3):398–412.CrossrefGoogle Scholar
  • Póczos B, Xiong L, Schneider J (2012) Nonparametric divergence estimation with applications to machine learning on distributions. Preprint, submitted February 14, https://arxiv.org/abs/1202.3758.Google Scholar
  • Popescu I (2005) A semidefinite programming approach to optimal-moment bounds for convex classes of distributions. Math. Oper. Res. 30(3):632–657.LinkGoogle Scholar
  • Qi M, Shen ZJ (2022) Integrating prediction/estimation and optimization with applications in operations management. INFORMS TutORials in Operations Research (INFORMS, Cantonsville, MD), 36–58.LinkGoogle Scholar
  • Rahimian H, Mehrotra S (2019) Distributionally robust optimization: A review. Preprint, submitted August 13, https://arxiv.org/abs/1908.05659.Google Scholar
  • Rockafellar RT (2015) Convex Analysis (Princeton University Press, Princeton, NJ).Google Scholar
  • Rothschild M, Stiglitz JE (1970) Increasing risk: I. A definition. J. Econom. Theory 2(3):225–243.CrossrefGoogle Scholar
  • Roy Chowdhury A, Lam H (2025) Do robustness and accuracy compete? Symmetry of uncertainty and superiority of naive optimization. Preprint.Google Scholar
  • Sadana U, Chenreddy A, Delage E, Forel A, Frejinger E, Vidal T (2025) A survey of contextual optimization methods for decision-making under uncertainty. Eur. J. Oper. Res. 320(2):271–289.CrossrefGoogle Scholar
  • Serfling RJ (2009) Approximation Theorems of Mathematical Statistics, vol. 162 (John Wiley & Sons, New York), 10.Google Scholar
  • Shafieezadeh-Abadeh S, Kuhn D, Esfahani PM (2019) Regularization via mass transportation. J. Machine Learn. Res. 20(103):1–68.Google Scholar
  • Shaked M, Shanthikumar JG (2007) Stochastic Orders (Springer Science & Business Media, New York).CrossrefGoogle Scholar
  • Shapiro A (1989) Asymptotic properties of statistical estimators in stochastic programming. Ann. Statist. 17(2):841–858.CrossrefGoogle Scholar
  • Shapiro A, Dentcheva D, Ruszczyński A (2014) Lectures on Stochastic Programming: Modeling and Theory (SIAM, Philadelphia).CrossrefGoogle Scholar
  • Sriperumbudur BK, Fukumizu K, Gretton A, Schölkopf B, Lanckriet GR (2012) On the empirical estimation of integral probability metrics. Electronic J. Statist. 6:1550–1599.CrossrefGoogle Scholar
  • Sutter T, Van Parys BP, Kuhn D (2020) A general framework for optimal data-driven optimization. Preprint, submitted October 13, https://arxiv.org/abs/2010.06606.Google Scholar
  • van der Vaart AW (2000) Asymptotic Statistics, vol. 3 (Cambridge University Press, Cambridge, UK).Google Scholar
  • van der Vaart AW, Wellner JA (1996) Weak Convergence and Empirical Processes (Springer, New York).CrossrefGoogle Scholar
  • Van Parys BP, Esfahani PM, Kuhn D (2020) From data to decisions: Distributionally robust optimization is optimal. Management Sci. 67(6):3387–3402.Google Scholar
  • Van Parys BP, Goulart PJ, Kuhn D (2016) Generalized Gauss inequalities via semidefinite programming. Math. Programming 156(1–2):271–302.CrossrefGoogle Scholar
  • Wang Q, Kulkarni SR, Verdú S (2009) Divergence estimation for multidimensional densities via k-nearest-neighbor distances. IEEE Trans. Inform. Theory 55(5):2392–2405.CrossrefGoogle Scholar
  • Wang Z, Glynn PW, Ye Y (2016) Likelihood robust optimization for data-driven problems. Comput. Management Sci. 13(2):241–261.CrossrefGoogle Scholar
  • Wiesemann W, Kuhn D, Sim M (2014) Distributionally robust convex optimization. Oper. Res. 62(6):1358–1376.LinkGoogle Scholar
  • Wilder B, Ewing E, Dilkina B, Tambe M (2019) End to end learning and optimization on graphs. Adv. Neural Inform. Processing Systems (Vancouver), vol. 32.Google Scholar
  • Wu D, Zhu H, Zhou E (2018) A Bayesian risk approach to data-driven stochastic optimization: Formulations and asymptotics. SIAM J. Optim. 28(2):1588–1612.CrossrefGoogle Scholar
  • Xu H, Mannor S (2012) Distributionally robust Markov decision processes. Math. Oper. Res. 37(2):288–300.LinkGoogle Scholar
  • Zeng Y, Lam H (2022) Generalization bounds with minimal dependency on hypothesis class via distributionally robust optimization. Adv. Neural Inform. Processing Systems (New Orleans), vol. 35, 27576–27590.Google Scholar
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