Stochastic Approximation Algorithms and Applications 论文
1998Journal of the American Statistical Association引用 1029
Neural Networks and ApplicationsDistributed Sensor Networks and Detection AlgorithmsStochastic Gradient Optimization Techniques
摘要
Applications and issues application to learning, state dependent noise and queueing applications to signal processing and adaptive control mathematical background convergence with probability one - Martingale difference noise convergence with probability one - correlated noise weak convergence - introduction weak convergence methods for general algorithms applications - proofs of convergence rate of convergence averaging of the iterates distributed/decentralized and asynchronous algorithms.