Optimisation via Monte Carlo Simulation. Annealing and all that. TBD.
Abernethy, Jacob, and Elad Hazan. 2016. “Faster Convex Optimization: Simulated Annealing with an Efficient Universal Barrier.” In International Conference on Machine Learning, 2520–28. PMLR.
Botev, Zdravko I., and Dirk P. Kroese. 2008. “An Efficient Algorithm for Rare-Event Probability Estimation, Combinatorial Optimization, and Counting.” Methodology and Computing in Applied Probability 10 (4): 471–505.
Dalalyan, Arnak S. 2017. “Further and Stronger Analogy Between Sampling and Optimization: Langevin Monte Carlo and Gradient Descent.” arXiv:1704.04752 [Math, Stat], April.
Devlin, Lee, Paul Horridge, Peter L Green, and Simon Maskell. 2021. “The No-U-Turn Sampler as a Proposal Distribution in a Sequential Monte Carlo Sampler with a Near-Optimal L-Kernel,” 5.
Drovandi, Christopher C., David J. Nott, and Daniel E. Pagendam. 2017. “New Insights into History Matching via Sequential Monte Carlo.” arXiv:1710.03133 [Stat].
Duan, Qibin, and Dirk P. Kroese. 2016. “Splitting for Optimization.” Computers & Operations Research 73 (C): 119–31.
Elvira, Víctor, and Emilie Chouzenoux. 2021. “Optimized Population Monte Carlo,” 13.
Freitas, J. F. G. de, Mahesan Niranjan, A. H. Gee, and Arnaud Doucet. 1998. “Sequential Monte Carlo Methods for Optimisation of Neural Network Models.” Cambridge University Engineering Department, Cambridge, England, Technical Report TR-328.
Goffe, William L., Gary D. Ferrier, and John Rogers. 1994. “Global Optimization of Statistical Functions with Simulated Annealing.” Journal of Econometrics 60 (1-2): 65–99.
Mandt, Stephan, Matthew D. Hoffman, and David M. Blei. 2017. “Stochastic Gradient Descent as Approximate Bayesian Inference.” JMLR, April.
Mıguez, Joaquın, Dan Crisan, and Petar M Djuric. 2010. “Sequential Monte Carlo Methods for the Optimization of a General Class of Objective Functions,” 32.
Robert, Christian P., and George Casella. 2004. Monte Carlo Statistical Methods. 2nd ed. Springer Texts in Statistics. New York: Springer.
Rubinstein, Reuven Y., and Dirk P. Kroese. 2016. Simulation and the Monte Carlo Method. 3 edition. Wiley series in probability and statistics. Hoboken, New Jersey: Wiley.
Rubinstein, Reuven Y, and Dirk P Kroese. 2004. The Cross-Entropy Method a Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation and Machine Learning. New York, NY: Springer New York.
Rubinstein, Reuven Y., Ad Ridder, and Radislav Vaisman. 2014. Fast Sequential Monte Carlo Methods for Counting and Optimization. Wiley Series in Probability and Statistics. Hoboken, New Jersey: Wiley.
No comments yet. Why not leave one?