Monte carlo optimisation

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, 2520–8. 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, 4): 471–505.

Duan, Qibin, and Dirk P. Kroese. 2016. “Splitting for Optimization.” Computers & Operations Research 73 (C, C): 119–31.

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.

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. 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., 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., 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.