Gaussian Markov Random Fields for Discrete Optimization via Simulation: Framework and Algorithms
Published Online:18 Jan 2019https://doi.org/10.1287/opre.2018.1778
References
- (1975) On computing certain elements of the inverse of a sparse matrix. Comm. ACM 18(3):177–179.Crossref, Google Scholar
- (2012) Tutorial: Optimization via simulation with Bayesian statistics and dynamic programming. Laroque C, Himmelspach J, Pasupathy R, Rose O, Uhrmacher AM, eds. Proc. 2012 Winter Simulation Conf. (IEEE, Piscataway, NJ), 1–16.Crossref, Google Scholar
- (2009) The knowledge-gradient policy for correlated normal beliefs. INFORMS J. Comput. 21(4):599–613.Link, Google Scholar
- (2009–2010) Software. Accessed July 14, 2018, https://people.orie.cornell.edu/pfrazier/src.html.Google Scholar
- (2006) Global optimization of stochastic black-box systems via sequential kriging metamodels. J. Global Optim. 34(3):441–466.Crossref, Google Scholar
- (1998) Efficient global optimization of expensive black-box functions. J. Global Optim. 13(4):455–492.Crossref, Google Scholar
- (2013) Statistical ranking and selection. Gass S, Fu M, eds. Encyclopedia of Operations Research and Management Science (Springer, New York), 1459–1469.Crossref, Google Scholar
- (2001) A fully sequential procedure for indifference-zone selection in simulation. ACM Trans. Model. Comput. Simulation 11(3):251–273.Crossref, Google Scholar
- (1985) A procedure for selecting a subset of sizemcontaining thelbest ofkindependent normal populations, with applications to simulation. Comm. Statist. B14(3):719–734.Crossref, Google Scholar
- (2010) Optimization via simulation over discrete decision variables. Tutorials Oper. Res. 7:193–207.Google Scholar
- (1983) On computing the inverse of a sparse matrix. Internat. J. Numer. Methods Engrg. 19(10):1513–1526.Crossref, Google Scholar
- (2013) Simulation optimization via kriging: A sequential search using expected improvement with computing budget constraints. IIE Trans. 45(7):763–780.Crossref, Google Scholar
- (2005) Gaussian Markov Random Fields: Theory and Applications (Chapman and Hall/CRC, New York).Crossref, Google Scholar
- (2014) Discrete optimization via simulation using Gaussian Markov random fields. Tolk A, Diallo SY, Ryzhov IO, Yilmaz L, Buckley S, Miller JA, eds. Proc. 2014 Winter Simulation Conf. (IEEE, Piscataway, NJ), 3809–3820.Crossref, Google Scholar
- (2013) Generalized integrated Brownian fields for simulation metamodeling. Pasupathy R, Kim SH, Tolk A, Hill R, Kuhl ME, eds. Proc. 2013 Winter Simulation Conf. (IEEE, Piscataway, NJ), 543–554.Crossref, Google Scholar
- (1973) Formation of a sparse bus impedance matrix and its application to short ciruit study. IEEE Power Engineering Society, eds. 8th PICA Conf. Proc. (IEEE, New York), 16–29.Google Scholar
- (2012) Modelling local and global phenomena with sparse Gaussian processes. Accessed July 14, 2018, https://arxiv.org/abs/1206.3290.Google Scholar
- (2000) Sequential design of computer experiments to minimize integrated response functions. Statistica Sinica 10(4):1133–1152.Google Scholar
- (1997) No free lunch theorems for optimization. Trans. Evolutionary Comput. 1(1):67–82.Crossref, Google Scholar
- (2016) Bayesian optimization via simulation with pairwise sampling and correlated prior beliefs. Oper. Res. 64(2):542–559.Link, Google Scholar
- (2010) Industrial strength COMPASS: A comprehensive algorithm and software for optimization via simulation. ACM Trans. Model. Comput. Simulation 20(1):1–29.Crossref, Google Scholar

