Multivariate stochastic approximation using a simultaneous perturbation gradient approximation
Reads0
Chats0
TLDR
The paper presents an SA algorithm that is based on a simultaneous perturbation gradient approximation instead of the standard finite-difference approximation of Keifer-Wolfowitz type procedures that can be significantly more efficient than the standard algorithms in large-dimensional problems.Abstract:
The problem of finding a root of the multivariate gradient equation that arises in function minimization is considered. When only noisy measurements of the function are available, a stochastic approximation (SA) algorithm for the general Kiefer-Wolfowitz type is appropriate for estimating the root. The paper presents an SA algorithm that is based on a simultaneous perturbation gradient approximation instead of the standard finite-difference approximation of Keifer-Wolfowitz type procedures. Theory and numerical experience indicate that the algorithm can be significantly more efficient than the standard algorithms in large-dimensional problems. >read more
Citations
More filters
Proceedings ArticleDOI
Mixed Simultaneous Perturbation Stochastic Approximation for Gradient-Free Optimization with Noisy Measurements
TL;DR: This work proposes the mixed simultaneous perturbation stochastic approximation (MSPSA) algorithm that uses the pseudo-gradient information to iteratively update the estimates while maintaining the mixed-variables-type constraints.
Journal ArticleDOI
A binary monkey search algorithm variation for solving the set covering problem
Broderick Crawford,Ricardo Soto,Rodrigo Olivares,Rodrigo Olivares,Gabriel Embry,Diego Flores,Wenceslao Palma,Carlos Castro,Fernando Paredes,José Miguel Rubio +9 more
TL;DR: This work presents an improved binary version of the monkey search algorithm for solving the set covering problem, naturally inspired by the cognitive behavior of monkeys for climbing mountains, with a new climbing process with a better exploratory capability and a new cooperation procedure to reduce the number of unfeasible solutions.
Journal ArticleDOI
A matrix-T approach to the sequential design of optimization experiments
TL;DR: In this article, a new approach to the sequential design of experiments for the rapid optimization of multiple response, multiple controllable factor processes is presented, which is based on an approximation of the cost to go of the underlying dynamic programming formulation.
Journal ArticleDOI
Simulation-Based Design of Urban Bi-modal Transport Systems
TL;DR: This paper considers the competition for limited road space and the operational characteristics, such as congestion occurrences, at the strategic design level and shows that the proposed methodology indeed designs a transport system which benefits the overall system's performance.
Journal ArticleDOI
Surrogate-Based Promising Area Search for Lipschitz Continuous Simulation Optimization
Qi Fan,Jiaqiao Hu +1 more
TL;DR: This work proposes an adaptive search algorithm for solving simulation optimization problems with Lipschitz continuous objective functions and combines the strength of several popular strategies in simulations.
References
More filters
Journal ArticleDOI
An Introduction to Probability Theory and Its Applications.
Journal ArticleDOI
Stochastic Estimation of the Maximum of a Regression Function
J. Kiefer,Jacob Wolfowitz +1 more
TL;DR: In this article, the authors give a scheme whereby, starting from an arbitrary point, one obtains successively $x_2, x_3, \cdots$ such that the regression function converges to the unknown point in probability as n \rightarrow \infty.
Journal ArticleDOI
Multidimensional Stochastic Approximation Methods
TL;DR: In this paper, a multidimensional stochastic approximation scheme is presented, and conditions are given for these schemes to converge a.s.p.s to the solutions of $k-stochastic equations in $k$ unknowns.
Journal ArticleDOI
Accelerated Stochastic Approximation
TL;DR: In this article, the Robbins-Monro procedure and the Kiefer-Wolfowitz procedure are considered, for which the magnitude of the $n$th step depends on the number of changes in sign in $(X_i - X_{i - 1})$ for n = 2, \cdots, n.