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: 10366.00 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: 12196.00 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: 12861.00 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.
Автор: Zaslavski Alexander J. Название: Turnpike Conditions in Infinite Dimensional Optimal Control ISBN: 3030201805 ISBN-13(EAN): 9783030201807 Издательство: Springer Рейтинг: Цена: 10366.00 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: 33686.00 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: 6707.00 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: 18294.00 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.
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.
Автор: Renato de Leone; Almerico Murli; Panos M. Pardalos Название: High Performance Algorithms and Software in Nonlinear Optimization ISBN: 0792354834 ISBN-13(EAN): 9780792354833 Издательство: Springer Рейтинг: Цена: 23168.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Contains a selection of papers presented at the conference on High Performance Software for Nonlinear Optimization (HPSNO97) which was held in Ischia, Italy, in June, 1997. This book provides an overview of the nonlinear optimization field, including algorithms, software evaluation, implementation issues, applications, and areas of research.
Ole Martin extends well-established techniques for the analysis of high-frequency data based on regular observations to the more general setting of asynchronous and irregular observations. Such methods are much needed in practice as real data usually comes in irregular form. In the theoretical part he develops laws of large numbers and central limit theorems as well as a new bootstrap procedure to assess asymptotic laws. The author then applies the theoretical results to estimate the quadratic covariation and to construct tests for the presence of common jumps. The simulation results show that in finite samples his methods despite the much more complex setting perform comparably well as methods based on regular data.
?About the Author:Dr. Ole Martin completed his PhD at the Kiel University (CAU), Germany. His research focuses on high-frequency statistics for semimartingales with the aim to develop methods based on irregularly observed data.
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