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Standard stochastic control methods assume that the probability distribution of uncertain variables is available.
P. A. Samuelson, “Lifetime portfolio selection by dynamic stochastic programming,” Rev. Econ. Stat. , vol. 51, no. 3, pp. 239–246, 1969
1969
Earlier work this paper cites.
N. H. Hakansson, “Optimal investment and consumption strategies under risk for a class of utility functions,” Econometrica , vol. 38, no. 5, pp. 587–607, 1970
1970
Earlier work this paper cites.
M. L. Puterman and M. C. Shin, “Modified policy iteration algorithms for discounted Markov decision problems,” Management Science , vol. 24, no. 11, pp. 1127–1137, 1978
1978
Earlier work this paper cites.
R. Reemtsen, “Discretization methods for the solution of semi-infinite programming problems,” J. Optim. Theory Appl. , vol. 71, no. 1, pp. 85–103, 1991
1991
Earlier work this paper cites.
R. Hettich and K. O. Kortanek, “Semi-infinite programming: Theory, methods, and applications,” SIAM Rev. , vol. 35, no. 3, pp. 380–429, 1993
1993
Earlier work this paper cites.
A. R. Bergen and V. Vittal, Power Systems Analysis . Pearson, 1999
1999
Earlier work this paper cites.
I. R. Petersen, M. R. James, and P. Dupuis, “Minimax optimal control of stochastic uncertain systems with relative entropy constraints,” IEEE Trans. Autom. Control , vol. 45, no. 3, pp. 398–412, 2000
2000
Earlier work this paper cites.
H. U. Küenle, “Stochastic games with complete information and average cost criteria,” in Advances in Dynamic Games and Applications . Birkhäuser, 2000, pp. 325–338
2000
Earlier work this paper cites.
J. I. González-Trejo, O. Hernández-Lerma, and L. F. Hoyos-Reyes, “Minimax control of discrete-time stochastic systems,” SIAM J. Control Optim. , vol. 41, no. 5, pp. 1626–1659, 2003
2003
Earlier work this paper cites.
A. Nilim and L. El Ghaoui, “Robust control of Markov decision processes with uncertain transition matrices,” Oper. Res. , vol. 53, no. 5, pp. 780–798, 2005
2005
Earlier work this paper cites.
G. Calafiore and M. C. Campi, “Uncertain convex programs: randomized solutions and confidence levels,” Math. Program., Ser. A , vol. 102, pp. 25–46, 2005
2005
Earlier work this paper cites.
J. E. Smith and R. L. Winkler, “The optimizer’s curse: Skepticism and postdecision surprise in decision analysis,” Manage. Sci. , vol. 52, no. 3, pp. 311–322, 2006
2006
Earlier work this paper cites.
I. Popescu, “Robust mean-covariance solutions for stochastic optimization,” Oper. Res. , vol. 55, no. 1, pp. 98–112, 2007
2007
Earlier work this paper cites.
M. López and G. Still, “Semi-infinite programming,” Eur. J. Oper. Res. , vol. 180, pp. 491–518, 2007
2007
Earlier work this paper cites.
E. Delage and Y. Ye, “Distributionally robust optimization under moment uncertainty with application to data-driven problems,” Oper. Res. , vol. 58, no. 3, pp. 595–612, 2010
2010
Earlier work this paper cites.
R. D. Zimmerman, C. E. Murillo-Sánchez, and R. J. Thomas, “MATPOWER: Steady-state operations, planning, and analysis tools for power systems research and education,” IEEE Transactions on Power Systems , vol. 26, no. 1, pp. 12–19, 2011
2011
Earlier work this paper cites.
H. Xu and S. Mannor, “Distributionally robust Markov decision processes,” Math. Oper. Res. , vol. 37, no. 2, pp. 288–300, 2012
2012
Cited alongside, same era.
D. P. Bertsekas, Dynamic Programming and Optimal Control, , 4th ed. Athena Scientific, 2012, vol. 2
2012
Cited alongside, same era.
F. L. Lewis, D. Vrabie, and V. L. Syrmos, Optimal Control . John Wiley & Sons, 2012
2012
Cited alongside, same era.
S. Zymler, D. Kuhn, and B. Rustem, “Distributionally robust joint chance constraints with second-order moment information,” Math. Program., Ser. A , vol. 137, pp. 167–198, 2013
2013
Cited alongside, same era.
A. Ben-Tal, D. Den Hertog, A. De Waegenaere, B. Melenberg, and G. Rennen, “Robust solutions of optimization problems affected by uncertain probabilities,” Manage. Sci. , vol. 59, no. 2, pp. 341–357, 2013
2013
Cited alongside, same era.
S. Samuelson and I. Yang, “Data-driven distributionally robust control of energy storage to manage wind power fluctuations,” in Proceedings of the 1st IEEE Conference on Control Technology and Applications , 2017
2017
Later among the works it cites.
I. Yang, “A convex optimization approach to distributionally robust Markov decision processes with Wasserstein distance,” IEEE Control Syst. Lett. , vol. 1, no. 1, pp. 164–169, 2017
2017
Later among the works it cites.
I. Yang, “Distributionally robust stochastic control with conic confidence sets,” in Proc. 56th IEEE Conf. Decis. Control , 2017
2017
Later among the works it cites.
G. Fazelnia, R. Madani, A. Kalbat, and J. Lavaei, “Convex relaxation for optimal distributed control problems,” IEEE Transactions on Automatic Control , vol. 62, no. 1, pp. 206–221, 2017
2017
Later among the works it cites.
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F. Dörfler and F. Bullo, “Novel insights into lossless AC and DC power flow,” in Proceedings of the 2013 Power and Energy Society General Meeting , 2013
2013
Cited alongside, same era.
W. Wiesemann, D. Kuhn, and M. Sim, “Distributionally robust convex optimization,” Oper. Res. , vol. 62, no. 6, pp. 1358–1376, 2014
2014
Cited alongside, same era.
M. L. Puterman, Markov Decision Processes: Discrete Stochastic Dynamic Programming . John Wiley & Sons, 2014
2014
Cited alongside, same era.
F. Dörfler, M. R. Jovanović, M. Chertkov, and F. Bullo, “Sparsity-promoting optimal wide-area control of power networks,” IEEE Transactions on Power Systems , vol. 29, no. 5, pp. 2281–2291, 2014
2014
Cited alongside, same era.
N. Fournier and A. Guillin, “On the rate of convergence in Wasserstein distance of the empirical measure,” Probab. Theory Relat. Fields , vol. 162, no. 3–4, pp. 707–738, 2015
2015
Cited alongside, same era.
2016
Cited alongside, same era.
R. Jiang and Y. Guan, “Data-driven chance constrained stochastic program,” Math. Program., Ser. A , vol. 158, pp. 291–327, 2016
2016
Cited alongside, same era.
P. Mohajerin Esfahani and D. Kuhn, “Data-driven distributionally robust optimization using the Wasserstein metric: Performance guarantees and tractable reformulations,” Math. Program. , vol. 171, no. 1–2, pp. 115–166, 2018
2018
Closest in time.
C. Zhao and Y. Guan, “Data-driven risk-averse stochastic optimization with Wasserstein metric,” Oper. Res. Lett. , vol. 46, no. 2, 2018
2018
Closest in time.
2018
Closest in time.
A. Sinha, H. Namkoong, and J. Duchi, “Certifying some distributional robustness with principled adversarial training,” in International Conference on Learning Representations , 2018
2018
Closest in time.
R. Chen and I. C. Paschalidis, “A robust learning approach for regression models based on distributionally robust optimization,” Journal of Machine Learning Research , pp. 1–48, 2018
2018
Closest in time.
S. Shafieezadeh-Abadeh, V. A. Nguyen, D. Kuhn, and P. Mohajerin Esfahani, “Wasserstein distributionally robust Kalman filtering,” in Neural Information Processing Systems , 2018
2018
Closest in time.
I. Yang, “A dynamic game approach to distributionally robust safety specifications for stochastic systems,” Automatica , vol. 94, pp. 94–101, 2018
2018
Closest in time.
N. Saldi, T. Linder, and S. Yüksel, Finite Approximations in Discrete-Time Stochastic Control: Quantized Models and Asymptotic Optimality . Birkhäuser, 2018
2018
Closest in time.
I. Tzortzis, C. D. Charalambous, and T. Charalambous, “Infinite horizon average cost dynamic programming subject to total variation distance ambiguity,” SIAM J. Control Optim. , vol. 57, no. 4, pp. 2843–2872, 2019
2019
Closest in time.
K. Kim and I. Yang, “Minimax control of ambiguous linear stochastic systems using the Wasserstein metric,” in Proc. 59th IEEE Conf. Decis. Control , 2020
2020
Closest in time.
I. Yang, “A convex optimization approach to dynamic programming in continuous state and action spaces,” J. Optimiz. Theory App. , vol. 187, pp. 133–157, 2020
2020
Closest in time.