Stochastic approximation and large deviations: upper bounds and w.p.1 convergence
SIAM Journal on Control and Optimization
Adaptive algorithms and stochastic approximations
Adaptive algorithms and stochastic approximations
Adaptive filter theory (2nd ed.)
Adaptive filter theory (2nd ed.)
Rate of convergence of recursive estimators
SIAM Journal on Control and Optimization
A Dynamical System Approach to Stochastic Approximations
SIAM Journal on Control and Optimization
Stochastic approximation with two time scales
Systems & Control Letters
Asynchronous Stochastic Approximations
SIAM Journal on Control and Optimization
Information rules: a strategic guide to the network economy
Information rules: a strategic guide to the network economy
Some Pathological Traps for Stochastic Approximation
SIAM Journal on Control and Optimization
The O.D. E. Method for Convergence of Stochastic Approximation and Reinforcement Learning
SIAM Journal on Control and Optimization
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
On the Convergence, Lock-In Probability, and Sample Complexity of Stochastic Approximation
SIAM Journal on Control and Optimization
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For a stochastic approximation-type recursion with finitely many possible limit points, we find a lower bound on the probability of converging to a prescribed point in its ‘domain of attraction’. This has implications for the lock-in phenomena in the stochastic models of increasing return economics and the sample complexity of stochastic approximation algorithms in engineering.