Stochastic approximation algorithms: overview and recent trends.
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Publication:5955825
DOI10.1007/BF02823149zbMath1075.62608OpenAlexW2089614885MaRDI QIDQ5955825
Publication date: 18 February 2002
Published in: Sādhanā (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/bf02823149
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Cites Work
- Markov chains and stochastic stability
- Nonconvergence to unstable points in urn models and stochastic approximations
- Infinitesimal and finite perturbation analysis for queueing networks
- A Newton-Raphson version of the multivariate Robbins-Monro procedure
- Large deviations analysis of some recursive algorithms with state dependent noise
- Distributed computation of fixed points of \(\infty\)-nonexpansive maps
- New method of stochastic approximation type
- Identification and stochastic adaptive control
- Ordinal optimization of DEDS
- Transformation of observations in stochastic approximation
- Stochastic approximation methods for constrained and unconstrained systems
- Strong convergence of a stochastic approximation algorithm
- A strong approximation theorem for stochastic recursive algorithms
- Learning mixed equilibria
- Asynchronous stochastic approximation and Q-learning
- Convergence of solutions to equations arising in neural networks
- Learning through reinforcement and replicator dynamics
- Weak convergence of recursions
- Stochastic approximation with two time scales
- On optimal estimation methods using stochastic approximation procedures
- On the almost sure asymptotic behaviour of stochastic algorithm
- \({\mathcal Q}\)-learning
- Continuous action set learning automata for stochastic optimization
- Learning dynamics in games with stochastic perturbations
- Do stochastic algorithms avoid traps?
- Pseudogradient adaptation and training algorithms
- Random optimization
- Adaption and learning in automatic systems. Translated by Z. J. Nikolic
- Chaotic relaxation
- Convergence of a class of random search algorithms
- Passive stochastic approximation
- A LEARNING ALGORITHM FOR DISCRETE-TIME STOCHASTIC CONTROL
- A stochastic method for global optimization
- Recursive self-tuning control of finite Markov chains
- Stochastic approximation method with gradient averaging for unconstrained problems
- A generalized URN problem and its applications
- Stochastic Approximations via Large Deviations: Asymptotic Properties
- Stochastic Minimization with Constant Step-Size: Asymptotic Laws
- Distributed asynchronous deterministic and stochastic gradient optimization algorithms
- Learning Optimal Discriminant Functions through a Cooperative Game of Automata
- Smoothed (conditional) perturbation analysis of discrete event dynamical systems
- Nonparametric sequential estimation of zeros and extrema of regression functions
- Asymptotic Properties of Distributed and Communicating Stochastic Approximation Algorithms
- Extensions of infinitesimal perturbation analysis
- Stochastic approximation algorithms for parallel and distributed processing
- Gradient approach for recursive estimation and control in finite Markov chains
- A fundamental approach to the convergence analysis of least squares algorithms
- Minimization by Random Search Techniques
- Recursive Stochastic Algorithms for Global Optimization in $\mathbb{R}^d $
- On extensions of Polyak's averaging approach to stochastic approximation
- Multivariate stochastic approximation using a simultaneous perturbation gradient approximation
- Acceleration of Stochastic Approximation by Averaging
- On positive real transfer functions and the convergence of some recursive schemes
- Analysis of recursive stochastic algorithms
- Some Pathological Traps for Stochastic Approximation
- Recursive algorithms, urn processes and chaining number of chain recurrent sets
- Stochastic optimization of regenerative systems using infinitesimal perturbation analysis
- On the Convergence of Stochastic Iterative Dynamic Programming Algorithms
- An analysis of temporal-difference learning with function approximation
- Stochastic differential equations: singularity of coefficients, regression models, and stochastic approximation
- Weighted Means in Stochastic Approximation of Minima
- Annealing of Iterative Stochastic Schemes
- Convergence Rate of Stochastic Approximation Algorithms in the Degenerate Case
- Asynchronous Stochastic Approximations
- The dynamic system method and the traps
- Two Timescale Analysis of the Alopex Algorithm for Optimization
- Gradient Convergence in Gradient methods with Errors
- Stochastic approximation with random truncations, state-dependent noise and discontinuous dynamics
- Convergence of sa algorithms in multi-root or multi-extreme cases
- Simulation-based optimization of Markov reward processes
- Equivalent necessary and sufficient conditions on noise sequences for stochastic approximation algorithms
- General results on the convergence of stochastic algorithms
- Stochastic Approximation and Large Deviations: Upper Bounds and <scp>w.p.1</scp> Convergence
- Stochastic approximation with averaging and feedback: rapidly convergent "on-line" algorithms
- Asymptotically optimal rate of convergence of smoothed stochastic recursive algorithms
- Probability metrics and recursive algorithms
- Passive stochastic approximation with constant step size and window width
- A Dynamical System Approach to Stochastic Approximations
- An alternative proof for convergence of stochastic approximation algorithms
- Actor-Critic--Type Learning Algorithms for Markov Decision Processes
- A two Timescale Stochastic Approximation Scheme for Simulation-Based Parametric Optimization
- Equation of State Calculations by Fast Computing Machines
- Multiscale Stochastic Approximation for Parametric Optimization of Hidden Markov Models
- Smoothing Derivatives of Functions and Applications
- Stochastic Estimation of the Maximum of a Regression Function
- A Stochastic Approximation Method
- A tutorial survey of reinforcement learning
- Learning automata algorithms for pattern classification.
- Some results characterizing the finite time behaviour of the simulated annealing algorithm.
- Structure theorems for partially asynchronous iterations of a nonnegative matrix with random delays.
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