Gradient estimation via perturbation analysis

by Paul Glasserman

Publisher: Kluwer Academic in Boston, London

Written in English
Published: Pages: 221 Downloads: 521
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Edition Notes

Includes bibliographical references (p. 211-218) and index.

Statementby Paul Glasserman ; forword by Yu-Chi Ho.
SeriesThe Kluwer international series in engineering and computer science
The Physical Object
Paginationxiv, 221p. :
Number of Pages221
ID Numbers
Open LibraryOL22496852M
ISBN 100792390954

Stochastic Gradient Estimation (book, handbook chapter) • Something New: Global Optimization Algorithm Output performance measures estimated via stochastic simulation that is EXPENSIVE, (nonlinear, possibly nondifferentiable) Inflnitesimal Perturbation Analysis. Analysis and Improvement of Policy Gradient Estimation Tingting Zhao, Hirotaka Hachiya, Gang Niu, and Masashi Sugiyama Tokyo Institute of Technology [email protected], [email protected], [email protected], [email protected] Abstract Policy gradient is a useful model-free reinforcement learning approach, but it tends to suffer from instability of gradient Cited by: 6. Analysis and Improvement of Policy Gradient Estimation 3 Problem Formulation Let us consider a Markov decision problem specified by (S,A,PT,PI,r,γ), where Sis aset of ℓ-dimensional continuous states, Ais a set of continuous actions, PT(s′|s,a) is the transition probability density from current state s to next state s′ when action ais taken, PI(s) is the probability of initial.   Augmented Infinitesimal Perturbation Analysis is used to determine asymptotically unbiased and strong consistent gradient estimates for use in the capacity planning of intree ATM networks. These gradients are used to determine the locally optimal minimum average network delay by applying a steepest descent algorithm with projection and an Armijo line search to .

Stochastic Approximation (SA) and Gradient Estimation • Gradient Estimation: –e.g. Finite differences (easiest, but biased): make a small perturbation in each dimension –Others yield unbiased estimates in special cases. • Pros: Fast, works on (simple) constrained problemsFile Size: 3MB. provide a direct way of calculating gradient estimates. 1 INTRODUCTION Infinitesimal perturbation analysis (IPA) is a technique for estimating the gradient of a system performance measure by observing the sample path from a single simulation run (Ho et al. , Glasserman , Ho and Cao , Fu and Hu. Gradient and Grid Perturbation Grid Perturbation • In order to compute this contribution, regeneration of the grid is required based on perturbations on the surface. • The grid regeneration is needed for every surface perturbation. • This procedure can be costly if the geometry is three-dimensional and complex, and would have to be repeated a number of times proportional to theFile Size: KB. Perturbation Analysis of Optimization Problems J. Frederic Bonnans1 and Alexander Shapiro2 1INRIA-Rocquencourt, Domaine de Voluceau, B.P. , Rocquencourt, France, and Ecole Polytechnique, France 2School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia , USA.

their complexity analysis. This book is meant to be something in between, a book on general convex optimization that focuses on problem formulation and modeling. We should also mention what this book is not. It is not a text primarily about convex analysis, or the mathematics of convex optimization; several existing texts cover these topics well. Scaling up M-estimation via sampling designs: the Horvitz-Thompson stochastic gradient descent Asymptotic analysis of Horvitz-Thompson estimators based on survey data (see [1]) has received a good deal of attention, in particular in the context of mean estimation and regression. Many vibration problems in engineering are nonlinear in nature. The usual linear analysis may be inadequate for many applications. An essential difference in the study of nonlinear systems is that general solutions cannot be obtained by superposition, as in the case of linear systems. Moreover, the nonlinearity brings many new phenomena, which do not occur in linear systems. bation analysis (cf. Ho and Cao , Glasserman ). Even in the field ofperturbation analysis, dif­ ferent techniques have been developed. For gradient estimation, the "original" technique is infinitesimal perturbation analysis (IPA), which remains the eas­ iest PA technique to apply in practice. However, its.

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Gradient estimation via perturbation analysis. [Paul Glasserman] Home. WorldCat Home About WorldCat Help. Search. Search for Library Items Search for Lists Search for Book, Internet Resource: All Authors / Contributors: Paul Glasserman.

Find more information about: ISBN: Gradient Estimation Via Perturbation Analysis (The Springer International Series in Engineering and Computer Science) st Edition by Paul Glasserman (Author) › Visit Amazon's Paul Glasserman Gradient estimation via perturbation analysis book.

Find all the books, read about the author, and more. Cited by: Read "Gradient estimation via perturbation analysis, by Paul Glasserman, Kluwer, Boston.

MA,pp., Networks: An International Journal" on DeepDyve, the largest online rental service for scholarly research with thousands of academic publications available at your fingertips. Gradient Estimation Via Perturbation Analysis. Authors: Glasserman, Paul Buy this book Hardcover ,59 *immediately available upon purchase as print book shipments may be delayed due to the COVID crisis.

ebook access is temporary and does not include ownership of the ebook. Only valid for books with an ebook : Springer US. Abstract. Perturbation analysis (PA) is the core of the gradient-based (or policy gradient) learning and optimization approach. The basic principle of PA is that the derivative of a system’s performance with respect to a parameter of the system can be decomposed into the sum of many small building blocks, each of which measures the effect of a single perturbation on the.

Gradient Estimation Via Perturbation Analysis. 点击放大图片 出版社: Springer. 作者: Ho Yu-Chi 出版时间: 年12月01 日. 10位国际标准书号: 13位国际标准. Gradient Estimation in Global Optimization Algorithms Megan Hazen, Member, IEEE and Maya R.

Gupta, Member, IEEE Abstract—The role of gradient estimation in global opti-mization is investigated. The concept of a regional gradient is introduced as a tool for analyzing and comparing different types of gradient estimates.

The field of perturbation analysis for gradient estimation includes numerous other extensions and variations on IPA not discussed here, including rare perturbation analysis (Brémaud and Vázquez-Abad, ); structural IPA (Dai and Ho, ); discontinuous perturbation analysis ; and augmented IPA (Gaivoronski et al., ).Cited by: In the case here, direct gradient estimation techniques (such as perturbation analysis and likelihood ratio methods) whenever applicable, are shown Author: Sujin Kim.

On gamma estimation via matrix kriging. and stochastic gradient estimation methods are used to estimate them given the market parameters. In practice, the surface (function) of. Hong () [Hong LJ () Estimating quantile sensitivities. Oper. Res. 57(1)] introduced a general framework based on probability sensitivities and a conditional expectation relationship for estimating quantile sensitivities by infinitesimal perturbation analysis (IPA).

We present an alternative more direct derivation of the IPA estimators that leads to simplified Cited by: 9.

The primary application is gradient estimation during the simulation of discrete-event systems, for example, queueing and inventory systems. Besides their importance in sensitivity analysis, these gradient estimators are a critical component in gradient-based simulation optimization methods.

gradient algorithm [22]. Nomenclature. The methods of estimating gradients of expectations have been independently pro-posed in several different fields, which use differing terminology.

What we call the score function estimator (via [3]) is alternatively called the likelihood ratio estimator [5] and REINFORCE [26].File Size: KB. Get this from a library. Conditional Monte Carlo: Gradient Estimation and Optimization Applications. [Michael Fu; Jianqiang Hu] -- Conditional Monte Carlo: Gradient Estimation and Optimization Applications deals with various gradient estimation techniques of perturbation analysis based on the use of conditional expectation.

The. We study the links between the likelihood-ratio (LR) gradient-estimation technique (sometimes called the score-function (SF) method), and infinitesimal perturbation analysis (IPA).

We show how IPA can be viewed as a (degenerate) special case of the LR and SF techniques by selecting an appropriate representation of the underlying sample space Cited by: Convergence (with probability one) of a stochastic optimization algorithm for a single server queue is proved.

The parameter to be optimized is updated using an infinitesimal perturbation analysis estimate of the gradient of the performance measure, and the updates are performed at general times.

First, an algorithm in which the parameter is updated before each customer begins Cited by: Next article in issue: Gradient estimation via perturbation analysis, by Paul Glasserman, Kluwer, Boston.

MA,pp. View issue TOC. Simultaneous perturbation stochastic approximation (SPSA) is an algorithmic method for optimizing systems with multiple unknown is a type of stochastic approximation algorithm. As an optimization method, it is appropriately suited to large-scale population models, adaptive modeling, simulation optimization, and atmospheric examples are.

Simulation Optimization of Traffic Light Signal Timings via Perturbation Analysis, working paper (with W.C. Howell). PDF Online Traffic Light Control Through Gradient Estimation Using Stochastic Fluid Models, Proceedings of the IFAC 16th Triennial World Congress, (with C.

Panayiotou, W.C. Howell). Gradient Estimation with Simultaneous Perturbation and Compressive Sensing (Gradient Estimation) Vivek S. Borkar, Vikranth R. Dwaracherla, Neeraja Sahasrabudhe Abstract We propose a scheme for nding a \good" estimator for the gradient of a function on a high-dimensional space with few function evaluations.

Often such functions are not sensitive. In this survey paper, we provide an overview on various simulation methods for Lévy processes. In addition, we introduce two simulation based sensitivity estimation methods: perturbation analysis and the likelihood ratio method.

Sensitivity estimation is useful in various applications, such as derivative pricing and parameter : Rachel Chen, Jianqiang Hu, Yijie Peng. Over the last two decades, various perturbation analysis techniques have been developed to handle a large class of problems.

Coupling is a method of g Author: Liyi Dai. JAMES C. SPALL is a member of the Principal Professional Staff at the Johns Hopkins University, Applied Physics Laboratory, and is the Chair of the Applied and Computational Mathematics Program within the Johns Hopkins School of Engineering.

Spall has published extensively in the areas of control and statistics and holds two U.S. patents. Author: James C. Spall. Professor Glasserman’s first book, “Gradient Estimation via Perturbation Analysis” is a standard reference in simulation with over citations. His book “Monte Carlo Methods in Financial Engineering” is a standard reference not just within financial engineering circles, but also in.

Gradient-based optimization using the new approach for estimating the derivatives is applied to example problems from the literature. The solutions are superior to all previously published solutions and are obtained with very reasonable computer run times.

Additional advantages of a gradient-based approach are by: 3. of the techniques discussed in this book for the sensor fusion and sensor management application areas, my own expertise is not in the areas of aY.

Ho and X. Cao, Perturbation Analysis of Discrete Event Dynamic Systems, Kluwer Academic Publishers, Boston, ,Ordinal Optimization. Perturbation theory comprises mathematical methods for finding an approximate solution to a problem, by starting from the exact solution of a related, simpler problem.

A critical feature of the technique is a middle step that breaks the problem into "solvable" and "perturbation" parts. Perturbation theory is applicable if the problem at hand cannot be solved exactly, but can be.

Perturbation Analysis of Discrete Event Dynamic Systems Yu-Chi Ho, Xi-Ren Cao (auth.) Dynamic Systems (DEDS) are almost endless: military C31 Ilogistic systems, the emergency ward of a metropolitan hospital, back offices of large insurance and brokerage fums, service and spare part operations of multinational fums the point is the.

Perturbation Analysis (PA) and other single run gradient estimation techniques, on the other hand, takes the viewpoint that a single sample path or experiment on a DEDS contains inherently much more information about the system than conventional simulations utilize in their output analysis.

Two approaches of gradient analysis are reviewed: direct and indirect gradient analyses. The most popular hypotheses in gradient analysis are also discussed. Contemporary state of gra-dient analysis theory is presented and illustrated.

Possible application of different views of vegetation pattern is offered. BOOK. Fu, M.C. and Hu, J.Q., Conditional Monte Carlo: Gradient Estimation and Optimization Applications, Kluwer Academic Publishers, (order from here.)And here.You can write a book review and share your experiences. Other readers will always be interested in your opinion of the books you've read.

Whether you've loved the book or not, if you give your honest and detailed thoughts then people will find new books that are right for them.Conditional Monte Carlo: Gradient Estimation and Optimization Applications deals with various gradient estimation techniques of perturbation analysis based on the use of conditional expectation.

The primary setting is discrete-event stochastic simulation. This book presents applications to queueing and.