Stochastic Optimization on Continuous Domains With Finite-Time Guarantees by Markov Chain Monte Carlo Methods
Stochastic Optimization on Continuous Domains With Finite-Time Guarantees by Markov Chain Monte Carlo Methods
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DOI：
10.1109/tac.2010.2078170
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发表时间：
2010-12-01
影响因子：
6.8
通讯作者：
Maciejowski, Jan M.
中科院分区：
文献类型：
--
作者：
Lecchini-Visintini, Andrea;Lygeros, John;Maciejowski, Jan M.
我们介绍的马尔可夫链蒙特卡罗（MCMC）算法在解决全球随机优化问题定义在连续域的有限时间性能的界限。它表明，MCMC算法与有限时间的保证，可以开发与适当的选择的目标分布，并通过研究其收敛性的总变差范数。这项工作的灵感来自于统计学习理论中开发的具有已知精度和置信度的有限时间学习的概念。
We introduce bounds on the finite-time performance of Markov chain Monte Carlo (MCMC) algorithms in solving global stochastic optimization problems defined over continuous domains. It is shown that MCMC algorithms with finite-time guarantees can be developed with a proper choice of the target distribution and by studying their convergence in total variation norm. This work is inspired by the concept of finite-time learning with known accuracy and confidence developed in statistical learning theory.