Iteratively sampling scheme for stochastic optimization with variable number sample path
Authors: 
Shuang Hao, Dali Zhang, Ming DongAuthors Info & Claims
References
[1]
C. Audet, K.J. Dzahini, M. Kokkolaras, S. Le Digabel, Stochastic mesh adaptive direct search for blackbox optimization using probabilistic estimates, Comput. Optim. Appl. 79 (2021) 1–34.
[2]
F. Bastin, C. Cirillo, P.L. Toint, An adaptive Monte Carlo algorithm for computing mixed logit estimators, Comput. Manag. Sci. 3 (1) (2006) 55–79.
[3]
G. Bayraksan, D.P. Morton, A sequential sampling procedure for stochastic programming, Oper. Res. 59 (4) (2011) 898–913,.
[4]
H. Chen, B.W. Schmeiser, Stochastic root finding via retrospective approximation, AIIE Trans. 33 (3) (2001) 259–275,.
[5]
R. Chen, M. Menickelly, K. Scheinberg, Stochastic optimization using a trustregion method and random models, Math. Program. 169 (2) (2018) 447–487.
[6]
X. Chen, C.T. Kelley, Optimization with hidden constraints and embedded Monte Carlo computations, Optim. Eng. 17 (1) (2016) 157–175,.
[7]
G. Deng, M.C. Ferris, Variable-number sample-path optimization, Math. Program. 117 (1–2) (2009) 81–109,.
[8]
N.I.M. Gould, D. Orban, P.L. Toint, Cutest: a constrained and unconstrained testing environment with safe threads for mathematical optimization, Comput. Optim. Appl. 60 (3) (2015) 545–557,.
[9]
T. Homem-de Mello, Variable-sample methods for stochastic optimization, ACM Trans. Model. Comput. Simul. 13 (2) (2003) 108–133,.
[10]
S. Kim, D. Zhang, Convergence Properties of Direct Search Methods for Stochastic Optimization, 2010, pp. 1003–1011. https://doi.org/10.1109/WSC.2010.5679089.
[11]
T.G. Kolda, R.M. Lewis, V. Torczon, Optimization by direct search: new perspectives on some classical and modern methods, SIAM Rev. 45 (3) (2003) 385–482,.
[12]
J. Larson, M. Menickelly, S.M. Wild, Derivative-free optimization methods, Acta Numer. 28 (2019) 287–404,.
[13]
R.M. Lewis, V. Torczon, M.W. Trosset, Direct search methods: then and now, J. Comput. Appl. Math. 124 (1–2) (2000) 191–207,.
[14]
R. Pasupathy, On choosing parameters in retrospective-approximation algorithms for stochastic root finding and simulation optimization, Oper. Res. 58 (2010) 889,.
[15]
R. Pasupathy, B.W. Schmeiser, Retrospective-approximation algorithms for the multidimensional stochastic root-finding problem, ACM Trans. Model. Comput. Simul. 19 (2) (2009),.
[16]
J.O. Royset, On sample size control in sample average approximations for solving smooth stochastic programs, Comput. Optim. Appl. 55 (2) (2013) 265–309,. cited by 9.
[17]
A. Shapiro, T. Homem-de Mello, A simulation-based approach to two-stage stochastic programming with recourse, Math. Program., Ser. B 81 (3) (1998) 301–325,.
[18]
A. Shapiro, D. Dentcheva, A. Ruszczynski, Lectures on Stochastic Programming, second edition, SIAM, Philadelphia, PA, USA, 2014.
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Elsevier B.V.
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Elsevier Science Publishers B. V.
Netherlands
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Published: 01 May 2022
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