Adaptive stochastic approximation by the simultaneous perturbation method
From MaRDI portal
Publication:2730250
DOI10.1109/TAC.2000.880982zbMath0990.93125OpenAlexW2565654137WikidataQ29012114 ScholiaQ29012114MaRDI QIDQ2730250
Publication date: 5 August 2001
Published in: IEEE Transactions on Automatic Control (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1109/tac.2000.880982
Related Items
Algorithm portfolios for noisy optimization ⋮ On stochastic extremum seeking via adaptive perturbation-demodulation loop ⋮ A theoretical and empirical comparison of gradient approximations in derivative-free optimization ⋮ A combined direction stochastic approximation algorithm ⋮ Finite Difference Gradient Approximation: To Randomize or Not? ⋮ A modified second‐order SPSA optimization algorithm for finite samples ⋮ Application of stochastic approximation techniques in neural modelling and control ⋮ A gradient method for unconstrained optimization in noisy environment ⋮ Descent direction method with line search for unconstrained optimization in noisy environment ⋮ Stochastic optimization using a trust-region method and random models ⋮ A one-bit, comparison-based gradient estimator ⋮ An adaptive optimization scheme with satisfactory transient performance ⋮ ASTRO-DF: A Class of Adaptive Sampling Trust-Region Algorithms for Derivative-Free Stochastic Optimization ⋮ Theoretical connections between optimization algorithms based on an approximate gradient ⋮ SIMULATION-BASED OPTIMIZATION BY NEW STOCHASTIC APPROXIMATION ALGORITHM ⋮ On the efficient low cost procedure for estimation of high-dimensional prediction error covariance matrices ⋮ Online data‐driven control of variable speed wind turbines using the simultaneous perturbation stochastic approximation approach ⋮ Stochastic approximation with nondecaying gain: Error bound and data‐driven gain‐tuning ⋮ Adaptive stochastic approximation algorithm ⋮ Variance-constrained actor-critic algorithms for discounted and average reward MDPs ⋮ Detecting entanglement of unknown states by violating the Clauser-Horne-Shimony-Holt inequality ⋮ Simple and cumulative regret for continuous noisy optimization ⋮ Bayesian evidence test for precise hypotheses ⋮ Discriminative Bayesian filtering lends momentum to the stochastic Newton method for minimizing log-convex functions ⋮ Generalization of a result of Fabian on the asymptotic normality of stochastic approximation ⋮ From model-based control to data-driven control: survey, classification and perspective ⋮ Newton-based stochastic optimization using \(q\)-Gaussian smoothed functional algorithms ⋮ A simultaneous perturbation stochastic approximation algorithm for coupled well placement and control optimization under geologic uncertainty ⋮ Quasi-Newton smoothed functional algorithms for unconstrained and constrained simulation optimization ⋮ Efficient Monte Carlo computation of Fisher information matrix using prior information ⋮ Stochastic approximation ⋮ Simultaneous perturbation Newton algorithms for simulation optimization ⋮ Multidimensional stochastic approximation ⋮ On optimization algorithms for the reservoir oil well placement problem ⋮ A short note on SPSA techniques and their use in nonlinear bioprocess identification ⋮ Unbiased group-wise alignment by iterative central tendency estimations ⋮ Distributed Gauss-Newton optimization method for history matching problems with multiple best matches ⋮ Stochastic approximation: from statistical origin to big-data, multidisciplinary applications ⋮ Improved variational Bayes inference for transcript expression estimation ⋮ Simultaneous Perturbation Stochastic Approximation with Norm-Limited Update Vector ⋮ Parallel Simultaneous Perturbation Optimization ⋮ Performance analysis of the simultaneous perturbation stochastic approximation algorithm on the noisy sphere model ⋮ An ODE method to prove the geometric convergence of adaptive stochastic algorithms ⋮ Augmented state feedback for improving observability of linear systems with nonlinear measurements ⋮ New combinatorial direction stochastic approximation algorithms ⋮ Simulation Optimization: A Review and Exploration in the New Era of Cloud Computing and Big Data ⋮ Smoothed Functional Algorithms for Stochastic Optimization Using q -Gaussian Distributions