Statistical Performance Modeling And Optimization
DOWNLOAD
Download Statistical Performance Modeling And Optimization PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Statistical Performance Modeling And Optimization book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Statistical Performance Modeling And Optimization
DOWNLOAD
Author : Xin Li
language : en
Publisher: Now Publishers Inc
Release Date : 2007
Statistical Performance Modeling And Optimization written by Xin Li and has been published by Now Publishers Inc this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Computers categories.
Statistical Performance Modeling and Optimization reviews various statistical methodologies that have been recently developed to model, analyze and optimize performance variations at both transistor level and system level in integrated circuit (IC) design. The following topics are discussed in detail: sources of process variations, variation characterization and modeling, Monte Carlo analysis, response surface modeling, statistical timing and leakage analysis, probability distribution extraction, parametric yield estimation and robust IC optimization. These techniques provide the necessary CAD infrastructure that facilitates the bold move from deterministic, corner-based IC design toward statistical and probabilistic design. Statistical Performance Modeling and Optimization reviews and compares different statistical IC analysis and optimization techniques, and analyzes their trade-offs for practical industrial applications. It serves as a valuable reference for researchers, students and CAD practitioners.
Statistical Analysis And Optimization For Vlsi Timing And Power
DOWNLOAD
Author : Ashish Srivastava
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-04-04
Statistical Analysis And Optimization For Vlsi Timing And Power written by Ashish Srivastava and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-04-04 with Technology & Engineering categories.
Statistical Analysis and Optimization For VLSI: Timing and Power is a state-of-the-art book on the newly emerging field of statistical computer-aided design (CAD) tools. The very latest research in statistical timing and power analysis techniques is included, along with efforts to incorporate parametric yield as the key objective function during the design process. Included is the necessary mathematical background on techniques which find widespread use in current analysis and optimization. The emphasis is on algorithms, modeling approaches for process variability, and statistical techniques that are the cornerstone of the probabilistic CAD movement. The authors also describe recent optimization approaches to timing yield and contrast them to deterministic optimization. The work will enable new researchers in this area to come up to speed quickly, as well as provide a handy reference for those already working in CAD tool development.
Optimization Techniques In Statistics
DOWNLOAD
Author : Jagdish S. Rustagi
language : en
Publisher: Elsevier
Release Date : 2014-05-19
Optimization Techniques In Statistics written by Jagdish S. Rustagi and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-19 with Mathematics categories.
Statistics help guide us to optimal decisions under uncertainty. A large variety of statistical problems are essentially solutions to optimization problems. The mathematical techniques of optimization are fundamentalto statistical theory and practice. In this book, Jagdish Rustagi provides full-spectrum coverage of these methods, ranging from classical optimization and Lagrange multipliers, to numerical techniques using gradients or direct search, to linear, nonlinear, and dynamic programming using the Kuhn-Tucker conditions or the Pontryagin maximal principle. Variational methods and optimization in function spaces are also discussed, as are stochastic optimization in simulation, including annealing methods. The text features numerous applications, including: Finding maximum likelihood estimates, Markov decision processes, Programming methods used to optimize monitoring of patients in hospitals, Derivation of the Neyman-Pearson lemma, The search for optimal designs, Simulation of a steel mill. Suitable as both a reference and a text, this book will be of interest to advanced undergraduate or beginning graduate students in statistics, operations research, management and engineering sciences, and related fields. Most of the material can be covered in one semester by students with a basic background in probability and statistics. - Covers optimization from traditional methods to recent developments such as Karmarkars algorithm and simulated annealing - Develops a wide range of statistical techniques in the unified context of optimization - Discusses applications such as optimizing monitoring of patients and simulating steel mill operations - Treats numerical methods and applications - Includes exercises and references for each chapter - Covers topics such as linear, nonlinear, and dynamic programming, variational methods, and stochastic optimization
Performance Modeling Stochastic Networks And Statistical Multiplexing Second Edition
DOWNLOAD
Author : Ravi R. Mazumdar
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Performance Modeling Stochastic Networks And Statistical Multiplexing Second Edition written by Ravi R. Mazumdar and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Computers categories.
This monograph presents a concise mathematical approach for modeling and analyzing the performance of communication networks with the aim of introducing an appropriate mathematical framework for modeling and analysis as well as understanding the phenomenon of statistical multiplexing. The models, techniques, and results presented form the core of traffic engineering methods used to design, control and allocate resources in communication networks.The novelty of the monograph is the fresh approach and insights provided by a sample-path methodology for queueing models that highlights the important ideas of Palm distributions associated with traffic models and their role in computing performance measures. The monograph also covers stochastic network theory including Markovian networks. Recent results on network utility optimization and connections to stochastic insensitivity are discussed. Also presented are ideas of large buffer, and many sources asymptotics that play an important role in understanding statistical multiplexing. In particular, the important concept of effective bandwidths as mappings from queueing level phenomena to loss network models is clearly presented along with a detailed discussion of accurate approximations for large networks.
Integrated Circuit And System Design Power And Timing Modeling Optimization And Simulation
DOWNLOAD
Author : José Monteiro
language : en
Publisher: Springer
Release Date : 2010-02-06
Integrated Circuit And System Design Power And Timing Modeling Optimization And Simulation written by José Monteiro and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-02-06 with Computers categories.
This book constitutes the thoroughly refereed post-conference proceedings of 19th International Workshop on Power and Timing Modeling, Optimization and Simulation, PATMOS 2009, featuring Integrated Circuit and System Design, held in Delft, The Netherlands during September 9-11, 2009. The 26 revised full papers and 10 revised poster papers presented were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on variability & statistical timing, circuit level techniques, power management, low power circuits & technology, system level techniques, power & timing optimization techniques, self-timed circuits, low power circuit analysis & optimization, and low power design studies.
Integrated Circuit And System Design Power And Timing Modeling Optimization And Simulation
DOWNLOAD
Author : Rene van Leuken
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-02-04
Integrated Circuit And System Design Power And Timing Modeling Optimization And Simulation written by Rene van Leuken and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-02-04 with Computers categories.
This book constitutes the refereed proceedings of the 20th International Conference on Integrated Circuit and System Design, PATMOS 2010, held in Grenoble, France, in September 2010. The 24 revised full papers presented and the 9 extended abstracts were carefully reviewed and are organized in topical sections on design flows; circuit techniques; low power circuits; self-timed circuits; process variation; high-level modeling of poweraware heterogeneous designs in SystemC-AMS; and minalogic.
Markov Models Optimization
DOWNLOAD
Author : M.H.A. Davis
language : en
Publisher: CRC Press
Release Date : 1993-08-01
Markov Models Optimization written by M.H.A. Davis and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993-08-01 with Mathematics categories.
This book presents a radically new approach to problems of evaluating and optimizing the performance of continuous-time stochastic systems. This approach is based on the use of a family of Markov processes called Piecewise-Deterministic Processes (PDPs) as a general class of stochastic system models. A PDP is a Markov process that follows deterministic trajectories between random jumps, the latter occurring either spontaneously, in a Poisson-like fashion, or when the process hits the boundary of its state space. This formulation includes an enormous variety of applied problems in engineering, operations research, management science and economics as special cases; examples include queueing systems, stochastic scheduling, inventory control, resource allocation problems, optimal planning of production or exploitation of renewable or non-renewable resources, insurance analysis, fault detection in process systems, and tracking of maneuvering targets, among many others. The first part of the book shows how these applications lead to the PDP as a system model, and the main properties of PDPs are derived. There is particular emphasis on the so-called extended generator of the process, which gives a general method for calculating expectations and distributions of system performance functions. The second half of the book is devoted to control theory for PDPs, with a view to controlling PDP models for optimal performance: characterizations are obtained of optimal strategies both for continuously-acting controllers and for control by intervention (impulse control). Throughout the book, modern methods of stochastic analysis are used, but all the necessary theory is developed from scratch and presented in a self-contained way. The book will be useful to engineers and scientists in the application areas as well as to mathematicians interested in applications of stochastic analysis.
Statistical Inference Via Convex Optimization
DOWNLOAD
Author : Anatoli Juditsky
language : en
Publisher: Princeton University Press
Release Date : 2020-04-07
Statistical Inference Via Convex Optimization written by Anatoli Juditsky and has been published by Princeton University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-07 with Mathematics categories.
This authoritative book draws on the latest research to explore the interplay of high-dimensional statistics with optimization. Through an accessible analysis of fundamental problems of hypothesis testing and signal recovery, Anatoli Juditsky and Arkadi Nemirovski show how convex optimization theory can be used to devise and analyze near-optimal statistical inferences. Statistical Inference via Convex Optimization is an essential resource for optimization specialists who are new to statistics and its applications, and for data scientists who want to improve their optimization methods. Juditsky and Nemirovski provide the first systematic treatment of the statistical techniques that have arisen from advances in the theory of optimization. They focus on four well-known statistical problems—sparse recovery, hypothesis testing, and recovery from indirect observations of both signals and functions of signals—demonstrating how they can be solved more efficiently as convex optimization problems. The emphasis throughout is on achieving the best possible statistical performance. The construction of inference routines and the quantification of their statistical performance are given by efficient computation rather than by analytical derivation typical of more conventional statistical approaches. In addition to being computation-friendly, the methods described in this book enable practitioners to handle numerous situations too difficult for closed analytical form analysis, such as composite hypothesis testing and signal recovery in inverse problems. Statistical Inference via Convex Optimization features exercises with solutions along with extensive appendixes, making it ideal for use as a graduate text.
Stochastic Simulation Optimization An Optimal Computing Budget Allocation
DOWNLOAD
Author : Chun-hung Chen
language : en
Publisher: World Scientific
Release Date : 2010-06-04
Stochastic Simulation Optimization An Optimal Computing Budget Allocation written by Chun-hung Chen and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-06-04 with Computers categories.
With the advance of new computing technology, simulation is becoming very popular for designing large, complex and stochastic engineering systems, since closed-form analytical solutions generally do not exist for such problems. However, the added flexibility of simulation often creates models that are computationally intractable. Moreover, to obtain a sound statistical estimate at a specified level of confidence, a large number of simulation runs (or replications) is usually required for each design alternative. If the number of design alternatives is large, the total simulation cost can be very expensive.Stochastic Simulation Optimization addresses the pertinent efficiency issue via smart allocation of computing resource in the simulation experiments for optimization, and aims to provide academic researchers and industrial practitioners with a comprehensive coverage of OCBA approach for stochastic simulation optimization. Starting with an intuitive explanation of computing budget allocation and a discussion of its impact on optimization performance, a series of OCBA approaches developed for various problems are then presented, from the selection of the best design to optimization with multiple objectives. Finally, this book discusses the potential extension of OCBA notion to different applications such as data envelopment analysis, experiments of design and rare-event simulation.
High Performance Simulation Based Optimization
DOWNLOAD
Author : Thomas Bartz-Beielstein
language : en
Publisher: Springer
Release Date : 2019-06-01
High Performance Simulation Based Optimization written by Thomas Bartz-Beielstein and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-01 with Computers categories.
This book presents the state of the art in designing high-performance algorithms that combine simulation and optimization in order to solve complex optimization problems in science and industry, problems that involve time-consuming simulations and expensive multi-objective function evaluations. As traditional optimization approaches are not applicable per se, combinations of computational intelligence, machine learning, and high-performance computing methods are popular solutions. But finding a suitable method is a challenging task, because numerous approaches have been proposed in this highly dynamic field of research. That’s where this book comes in: It covers both theory and practice, drawing on the real-world insights gained by the contributing authors, all of whom are leading researchers. Given its scope, if offers a comprehensive reference guide for researchers, practitioners, and advanced-level students interested in using computational intelligence and machine learning to solve expensive optimization problems.