Bayesian and High-Dimensional Global Optimization, Zhigljavsky, Anatoly Zilinskas, Antanas
Автор: Nikeghbali Название: High-Dimensional Optimization and Probability ISBN: 3031008316 ISBN-13(EAN): 9783031008313 Издательство: Springer Рейтинг: Цена: 11878.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This volume presents extensive research devoted to a broad spectrum of mathematics with emphasis on interdisciplinary aspects of Optimization and Probability. Chapters also emphasize applications to Data Science, a timely field with a high impact in our modern society. The discussion presents modern, state-of-the-art, research results and advances in areas including non-convex optimization, decentralized distributed convex optimization, topics on surrogate-based reduced dimension global optimization in process systems engineering, the projection of a point onto a convex set, optimal sampling for learning sparse approximations in high dimensions, the split feasibility problem, higher order embeddings, codifferentials and quasidifferentials of the expectation of nonsmooth random integrands, adjoint circuit chains associated with a random walk, analysis of the trade-off between sample size and precision in truncated ordinary least squares, spatial deep learning, efficient location-based tracking for IoT devices using compressive sensing and machine learning techniques, and nonsmooth mathematical programs with vanishing constraints in Banach spaces. The book is a valuable source for graduate students as well as researchers working on Optimization, Probability and their various interconnections with a variety of other areas. Chapter 12 is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
Автор: Monique Florenzano; P. Gourdel; Cuong Le Van Название: Finite Dimensional Convexity and Optimization ISBN: 3642625703 ISBN-13(EAN): 9783642625701 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book discusses convex analysis, the basic underlying structure of argumentation in economic theory. The text is aimed at senior undergraduate students, graduate students, and specialists of mathematical programming who are undertaking research into applied mathematics and economics.
Описание: The aim of this work is to present in a unified approach a series of results concerning totally convex functions on Banach spaces and their applications to building iterative algorithms for computing common fixed points of mea- surable families of operators and optimization methods in infinite dimen- sional settings.
Автор: Dhara Название: Optimality Conditions in Convex Optimization ISBN: 113811524X ISBN-13(EAN): 9781138115248 Издательство: Taylor&Francis Рейтинг: Цена: 12248.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book explores an extremely important issue in convex optimization: optimality conditions. The text focuses on finite dimensions to allow for much deeper results and a better understanding of the structures involved in a convex optimization problem. The authors include examples, details of major results, and proofs of the main results.
This book provides a comprehensive study of turnpike phenomenon arising in optimal control theory. The focus is on individual (non-generic) turnpike results which are both mathematically significant and have numerous applications in engineering and economic theory. All results obtained in the book are new. New approaches, techniques, and methods are rigorously presented and utilize research from finite-dimensional variational problems and discrete-time optimal control problems to find the necessary conditions for the turnpike phenomenon in infinite dimensional spaces. The semigroup approach is employed in the discussion as well as PDE descriptions of continuous-time dynamics. The main results on sufficient and necessary conditions for the turnpike property are completely proved and the numerous illustrative examples support the material for the broad spectrum of experts. Mathematicians interested in the calculus of variations, optimal control and in applied functional analysis will find this book a useful guide to the turnpike phenomenon in infinite dimensional spaces. Experts in economic and engineering modeling as well as graduate students will also benefit from the developed techniques and obtained results.
Автор: Zaslavski Alexander J. Название: Turnpike Conditions in Infinite Dimensional Optimal Control ISBN: 3030201805 ISBN-13(EAN): 9783030201807 Издательство: Springer Рейтинг: Цена: 11878.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book provides a comprehensive study of turnpike phenomenon arising in optimal control theory.
Автор: Ohsaki, Makoto Название: Optimization of Finite Dimensional Structures ISBN: 1439820031 ISBN-13(EAN): 9781439820032 Издательство: Taylor&Francis Рейтинг: Цена: 31390.00 р. Наличие на складе: Нет в наличии.
Автор: Daniel Packwood Название: Bayesian Optimization for Materials Science ISBN: 9811067805 ISBN-13(EAN): 9789811067808 Издательство: Springer Рейтинг: Цена: 7685.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Finally, Chapter 3 discusses an application of Bayesian optimization to a complicated structure optimization problem in computational surface science.Bayesian optimization is a promising global optimization technique that originates in the field of machine learning and is starting to gain attention in materials science.
Автор: Jonas Mockus Название: A Set of Examples of Global and Discrete Optimization ISBN: 1461371147 ISBN-13(EAN): 9781461371144 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book shows how the Bayesian Approach (BA) improves well- known heuristics by randomizing and optimizing their parameters. A theoretical setting is described in which one can discuss a Bayesian adaptive choice of heuristics for discrete and global optimization prob- lems.
Although there are many Bayesian statistical books that focus on biostatistics and economics, there are few that address the problems faced by engineers. Bayesian Process Monitoring, Control and Optimization resolves this need, showing you how to oversee, adjust, and optimize industrial processes.
Bridging the gap between application and development, this reference adopts Bayesian approaches for actual industrial practices. Divided into four parts, it begins with an introduction that discusses inferential problems and presents modern methods in Bayesian computation. The next part explains statistical process control (SPC) and examines both univariate and multivariate process monitoring techniques. Subsequent chapters present Bayesian approaches that can be used for time series data analysis and process control. The contributors include material on the Kalman filter, radar detection, and discrete part manufacturing. The last part focuses on process optimization and illustrates the application of Bayesian regression to sequential optimization, the use of Bayesian techniques for the analysis of saturated designs, and the function of predictive distributions for optimization. Written by international contributors from academia and industry, Bayesian Process Monitoring, Control and Optimization provides up-to-date applications of Bayesian processes for industrial, mechanical, electrical, and quality engineers as well as applied statisticians.
Автор: Adcock, Ben Brugiapaglia, Simone Webster, Clayton G. Название: Sparse polynomial approximation of high-dimensional functions ISBN: 1611976871 ISBN-13(EAN): 9781611976878 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 10534.00 р. Наличие на складе: Поставка под заказ.
Описание: Over seventy years ago, Richard Bellman coined the term "the curse of dimensionality" to describe phenomena and computational challenges that arise in high dimensions. These challenges, in tandem with the ubiquity of high-dimensional functions in real-world applications, have led to a lengthy, focused research effort on high-dimensional approximation—that is, the development of methods for approximating functions of many variables accurately and efficiently from data. This book provides an in-depth treatment of one of the latest installments in this long and ongoing story: sparse polynomial approximation methods. These methods have emerged as useful tools for various high-dimensional approximation tasks arising in a range of applications in computational science and engineering. It begins with a comprehensive overview of best s-term polynomial approximation theory for holomorphic, high-dimensional functions, as well as a detailed survey of applications to parametric differential equations. It then describes methods for computing sparse polynomial approximations, focusing on least squares and compressed sensing techniques.Sparse Polynomial Approximation of High-Dimensional Functions presents the first comprehensive and unified treatment of polynomial approximation techniques that can mitigate the curse of dimensionality in high-dimensional approximation, including least squares and compressed sensing. It develops main concepts in a mathematically rigorous manner, with full proofs given wherever possible, and it contains many numerical examples, each accompanied by downloadable code. The authors provide an extensive bibliography of over 350 relevant references, with an additional annotated bibliography available on the book's companion website (www.sparse-hd-book.com).This text is aimed at graduate students, postdoctoral fellows, and researchers in mathematics, computer science, and engineering who are interested in high-dimensional polynomial approximation techniques.
ООО "Логосфера " Тел:+7(495) 980-12-10 www.logobook.ru
