Stochastic Gradient Estimation

Abstract

The author considers the problem of efficiently estimating gradients from stochastic simulation. Although the primary motivation is their use in simulation optimization, the resulting estimators also can be useful in other ways, such as in sensitivity analysis. The main approaches described are finite differences (including simultaneous perturbations), perturbation analysis, the likelihood ratio/score function method, and the use of weak derivatives. Three examples of simulation optimization are presented. These examples are a stochastic activity network, a single-server queue, and an inventory control system.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2005
Accession Number
ADA438511

Entities

People

  • Michael C. Fu

Organizations

  • University of Maryland

Tags

Communities of Interest

  • C4I
  • Human Systems

DTIC Thesaurus Topics

  • Algorithms
  • Computational Science
  • Computer Programming
  • Equations
  • Estimators
  • Information Science
  • Inventory Control
  • Markov Processes
  • Mathematical Programming
  • Monte Carlo Method
  • Operations Research
  • Probability
  • Random Variables
  • Sampling
  • Stochastic Processes
  • Supply Chain
  • Supply Chain Management

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Mathematical Modeling and Probability Theory.