Convergence of a stochastic approximation algorithm for the GI/G/1 queue using infinitesimal perturbation analysis

DOI10.1007/BF00941166zbMath0699.93087OpenAlexW2075061201MaRDI QIDQ913759

N. E. Zubov

Publication date: 1990

Published in: Journal of Optimization Theory and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf00941166

zbMATH Keywords

discrete-event systemsapproximation algorithmunbiased estimatorsgradient estimatorsRobbins-Monro type stochastic

Mathematics Subject Classification ID

Applications of mathematical programming (90C90) Stochastic programming (90C15) Queueing theory (aspects of probability theory) (60K25) Stochastic approximation (62L20) Stochastic systems in control theory (general) (93E03)

Related Items (11)

Infinitesimal perturbation analysis for second derivative estimation and design of manufacturing flow controllers ⋮ Optimal load sharing in soft real-time systems using likelihood ratios ⋮ Convergence of perturbation analysis based optimization algorithm with fixed number of customers period ⋮ Using stochastic optimization to determine threshold values for the control of unreliable manufacturing systems ⋮ Adaptative Monte Carlo Method, A Variance Reduction Technique ⋮ Regression Models Augmented with Direct Stochastic Gradient Estimators ⋮ Optimizing discrete event dynamic systems via the gradient surface method ⋮ Optimization via simulation: A review ⋮ State-dependent control of a single stage hybrid system with Poisson arrivals ⋮ Convergence of stochastic approximation coupled with perturbation analysis in a class of manufacturing flow control models ⋮ Simulation Optimization: A Review and Exploration in the New Era of Cloud Computing and Big Data

Cites Work

• Stochastic approximation methods for constrained and unconstrained systems
• Perturbation Analysis Gives Strongly Consistent Sensitivity Estimates for the M/G/1 Queue
• Simulating Stable Stochastic Systems: III. Regenerative Processes and Discrete-Event Simulations

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