Bayesian And High Dimensional Global Optimization

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Bayesian and High-Dimensional Global Optimization

Author : Anatoly Zhigljavsky
Publisher : Springer Nature
ISBN 13 : 3030647129
Total Pages : 125 pages
Book Rating : 4.0/5 (36 download)

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Book Synopsis Bayesian and High-Dimensional Global Optimization by : Anatoly Zhigljavsky

Download or read book Bayesian and High-Dimensional Global Optimization written by Anatoly Zhigljavsky and published by Springer Nature. This book was released on 2021-03-02 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt: Accessible to a variety of readers, this book is of interest to specialists, graduate students and researchers in mathematics, optimization, computer science, operations research, management science, engineering and other applied areas interested in solving optimization problems. Basic principles, potential and boundaries of applicability of stochastic global optimization techniques are examined in this book. A variety of issues that face specialists in global optimization are explored, such as multidimensional spaces which are frequently ignored by researchers. The importance of precise interpretation of the mathematical results in assessments of optimization methods is demonstrated through examples of convergence in probability of random search. Methodological issues concerning construction and applicability of stochastic global optimization methods are discussed, including the one-step optimal average improvement method based on a statistical model of the objective function. A significant portion of this book is devoted to an analysis of high-dimensional global optimization problems and the so-called ‘curse of dimensionality’. An examination of the three different classes of high-dimensional optimization problems, the geometry of high-dimensional balls and cubes, very slow convergence of global random search algorithms in large-dimensional problems , and poor uniformity of the uniformly distributed sequences of points are included in this book.

Bayesian Approach to Global Optimization

Author : Jonas Mockus
Publisher : Springer Science & Business Media
ISBN 13 : 9400909098
Total Pages : 267 pages
Book Rating : 4.4/5 (9 download)

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Book Synopsis Bayesian Approach to Global Optimization by : Jonas Mockus

Download or read book Bayesian Approach to Global Optimization written by Jonas Mockus and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 267 pages. Available in PDF, EPUB and Kindle. Book excerpt: ·Et moi ... si j'avait su comment en revcnir. One service mathematics has rendered the je o'y semis point alle.' human race. It has put common sense back Jules Verne where it beloogs. on the topmost shelf next to the dusty canister labelled 'discarded non The series is divergent; therefore we may be sense', able to do something with it. Eric T. BclI O. Heaviside Mathematics is a tool for thought. A highly necessary tool in a world where both feedback and non linearities abound. Similarly, all kinds of parts of mathematics serve as tools for other parts and for other sciences. Applying a simple rewriting rule to the quote on the right above one finds such statements as: 'One service topology has rendered mathematical physics ... '; 'One service logic has rendered com puter science .. .'; 'One service category theory has rendered mathematics .. .'. All arguably true. And all statements obtainable this way form part of the raison d'etre of this series.

The Bayesian Approach to Global Optimization

Author : Jonas Mockus
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (16 download)

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Book Synopsis The Bayesian Approach to Global Optimization by : Jonas Mockus

Download or read book The Bayesian Approach to Global Optimization written by Jonas Mockus and published by . This book was released on 1984 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

2021 12th International Conference on Information, Intelligence, Systems and Applications (IISA)

Author : IEEE Staff
Publisher :
ISBN 13 : 9781665400336
Total Pages : pages
Book Rating : 4.4/5 (3 download)

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Book Synopsis 2021 12th International Conference on Information, Intelligence, Systems and Applications (IISA) by : IEEE Staff

Download or read book 2021 12th International Conference on Information, Intelligence, Systems and Applications (IISA) written by IEEE Staff and published by . This book was released on 2021-07-12 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The International Conference on Information, Intelligence, Systems and Applications (IISA) series offers a forum for the constructive interaction and prolific exchange of ideas among scientists and practitioners from different research fields such as computers, mathematics, physics, biology, medicine, chemistry, experimental psychology, social sciences, linguistics, and engineering having the goal of developing methodologies and tools for the solution of complex problems in artificial intelligence, biology, neuroscience, security, monitoring, surveillance, healthcare, sustainability in energy sources, governance, education, commerce, automation, robotics, optimization, image, speech and natural languages, and their integration

Bayesian Optimization

Author : Roman Garnett
Publisher : Cambridge University Press
ISBN 13 : 1108623557
Total Pages : 376 pages
Book Rating : 4.1/5 (86 download)

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Book Synopsis Bayesian Optimization by : Roman Garnett

Download or read book Bayesian Optimization written by Roman Garnett and published by Cambridge University Press. This book was released on 2023-01-31 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian optimization is a methodology for optimizing expensive objective functions that has proven success in the sciences, engineering, and beyond. This timely text provides a self-contained and comprehensive introduction to the subject, starting from scratch and carefully developing all the key ideas along the way. This bottom-up approach illuminates unifying themes in the design of Bayesian optimization algorithms and builds a solid theoretical foundation for approaching novel situations. The core of the book is divided into three main parts, covering theoretical and practical aspects of Gaussian process modeling, the Bayesian approach to sequential decision making, and the realization and computation of practical and effective optimization policies. Following this foundational material, the book provides an overview of theoretical convergence results, a survey of notable extensions, a comprehensive history of Bayesian optimization, and an extensive annotated bibliography of applications.

Stochastic Global Optimization

Author : Anatoly Zhigljavsky
Publisher : Springer Science & Business Media
ISBN 13 : 0387747400
Total Pages : 262 pages
Book Rating : 4.3/5 (877 download)

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Book Synopsis Stochastic Global Optimization by : Anatoly Zhigljavsky

Download or read book Stochastic Global Optimization written by Anatoly Zhigljavsky and published by Springer Science & Business Media. This book was released on 2007-11-20 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book examines the main methodological and theoretical developments in stochastic global optimization. It is designed to inspire readers to explore various stochastic methods of global optimization by clearly explaining the main methodological principles and features of the methods. Among the book’s features is a comprehensive study of probabilistic and statistical models underlying the stochastic optimization algorithms.

High-Dimensional Optimization and Probability

Author : Ashkan Nikeghbali
Publisher : Springer Nature
ISBN 13 : 3031008324
Total Pages : 417 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis High-Dimensional Optimization and Probability by : Ashkan Nikeghbali

Download or read book High-Dimensional Optimization and Probability written by Ashkan Nikeghbali and published by Springer Nature. This book was released on 2022-08-04 with total page 417 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Bayesian Heuristic Approach to Discrete and Global Optimization

Author : Jonas Mockus
Publisher : Springer
ISBN 13 : 9781441947673
Total Pages : 0 pages
Book Rating : 4.9/5 (476 download)

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Book Synopsis Bayesian Heuristic Approach to Discrete and Global Optimization by : Jonas Mockus

Download or read book Bayesian Heuristic Approach to Discrete and Global Optimization written by Jonas Mockus and published by Springer. This book was released on 2010-12-07 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian decision theory is known to provide an effective framework for the practical solution of discrete and nonconvex optimization problems. This book is the first to demonstrate that this framework is also well suited for the exploitation of heuristic methods in the solution of such problems, especially those of large scale for which exact optimization approaches can be prohibitively costly. The book covers all aspects ranging from the formal presentation of the Bayesian Approach, to its extension to the Bayesian Heuristic Strategy, and its utilization within the informal, interactive Dynamic Visualization strategy. The developed framework is applied in forecasting, in neural network optimization, and in a large number of discrete and continuous optimization problems. Specific application areas which are discussed include scheduling and visualization problems in chemical engineering, manufacturing process control, and epidemiology. Computational results and comparisons with a broad range of test examples are presented. The software required for implementation of the Bayesian Heuristic Approach is included. Although some knowledge of mathematical statistics is necessary in order to fathom the theoretical aspects of the development, no specialized mathematical knowledge is required to understand the application of the approach or to utilize the software which is provided. Audience: The book is of interest to both researchers in operations research, systems engineering, and optimization methods, as well as applications specialists concerned with the solution of large scale discrete and/or nonconvex optimization problems in a broad range of engineering and technological fields. It may be used as supplementary material for graduate level courses.

Bayesian Heuristic Approach to Discrete and Global Optimization

Author : Jonas Mockus
Publisher : Springer
ISBN 13 : 9780792343271
Total Pages : 397 pages
Book Rating : 4.3/5 (432 download)

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Book Synopsis Bayesian Heuristic Approach to Discrete and Global Optimization by : Jonas Mockus

Download or read book Bayesian Heuristic Approach to Discrete and Global Optimization written by Jonas Mockus and published by Springer. This book was released on 1996-12-31 with total page 397 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian decision theory is known to provide an effective framework for the practical solution of discrete and nonconvex optimization problems. This book is the first to demonstrate that this framework is also well suited for the exploitation of heuristic methods in the solution of such problems, especially those of large scale for which exact optimization approaches can be prohibitively costly. The book covers all aspects ranging from the formal presentation of the Bayesian Approach, to its extension to the Bayesian Heuristic Strategy, and its utilization within the informal, interactive Dynamic Visualization strategy. The developed framework is applied in forecasting, in neural network optimization, and in a large number of discrete and continuous optimization problems. Specific application areas which are discussed include scheduling and visualization problems in chemical engineering, manufacturing process control, and epidemiology. Computational results and comparisons with a broad range of test examples are presented. The software required for implementation of the Bayesian Heuristic Approach is included. Although some knowledge of mathematical statistics is necessary in order to fathom the theoretical aspects of the development, no specialized mathematical knowledge is required to understand the application of the approach or to utilize the software which is provided. Audience: The book is of interest to both researchers in operations research, systems engineering, and optimization methods, as well as applications specialists concerned with the solution of large scale discrete and/or nonconvex optimization problems in a broad range of engineering and technological fields. It may be used as supplementary material for graduate level courses.

A Set of Examples of Global and Discrete Optimization

Author : Jonas Mockus
Publisher : Springer Science & Business Media
ISBN 13 : 1461546710
Total Pages : 318 pages
Book Rating : 4.4/5 (615 download)

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Book Synopsis A Set of Examples of Global and Discrete Optimization by : Jonas Mockus

Download or read book A Set of Examples of Global and Discrete Optimization written by Jonas Mockus and published by Springer Science & Business Media. This book was released on 2013-11-22 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book shows how the Bayesian Approach (BA) improves well known heuristics by randomizing and optimizing their parameters. That is the Bayesian Heuristic Approach (BHA). The ten in-depth examples are designed to teach Operations Research using Internet. Each example is a simple representation of some impor tant family of real-life problems. The accompanying software can be run by remote Internet users. The supporting web-sites include software for Java, C++, and other lan guages. A theoretical setting is described in which one can discuss a Bayesian adaptive choice of heuristics for discrete and global optimization prob lems. The techniques are evaluated in the spirit of the average rather than the worst case analysis. In this context, "heuristics" are understood to be an expert opinion defining how to solve a family of problems of dis crete or global optimization. The term "Bayesian Heuristic Approach" means that one defines a set of heuristics and fixes some prior distribu tion on the results obtained. By applying BHA one is looking for the heuristic that reduces the average deviation from the global optimum. The theoretical discussions serve as an introduction to examples that are the main part of the book. All the examples are interconnected. Dif ferent examples illustrate different points of the general subject. How ever, one can consider each example separately, too.

Bayesian Optimization in Action

Author : Quan Nguyen
Publisher : Simon and Schuster
ISBN 13 : 1633439070
Total Pages : 422 pages
Book Rating : 4.6/5 (334 download)

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Book Synopsis Bayesian Optimization in Action by : Quan Nguyen

Download or read book Bayesian Optimization in Action written by Quan Nguyen and published by Simon and Schuster. This book was released on 2023-11-14 with total page 422 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Optimization in Action teaches you how to build Bayesian Optimisation systems from the ground up. This book transforms state-of-the-art research into usable techniques you can easily put into practice. With a range of illustrations, and concrete examples, this book proves that Bayesian Optimisation doesn't have to be difficult!

Global Optimization

Author : Aimo Törn
Publisher :
ISBN 13 :
Total Pages : 274 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis Global Optimization by : Aimo Törn

Download or read book Global Optimization written by Aimo Törn and published by . This book was released on 1989 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Advances in Stochastic and Deterministic Global Optimization

Author : Panos M. Pardalos
Publisher : Springer
ISBN 13 : 3319299751
Total Pages : 296 pages
Book Rating : 4.3/5 (192 download)

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Book Synopsis Advances in Stochastic and Deterministic Global Optimization by : Panos M. Pardalos

Download or read book Advances in Stochastic and Deterministic Global Optimization written by Panos M. Pardalos and published by Springer. This book was released on 2016-11-04 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: Current research results in stochastic and deterministic global optimization including single and multiple objectives are explored and presented in this book by leading specialists from various fields. Contributions include applications to multidimensional data visualization, regression, survey calibration, inventory management, timetabling, chemical engineering, energy systems, and competitive facility location. Graduate students, researchers, and scientists in computer science, numerical analysis, optimization, and applied mathematics will be fascinated by the theoretical, computational, and application-oriented aspects of stochastic and deterministic global optimization explored in this book. This volume is dedicated to the 70th birthday of Antanas Žilinskas who is a leading world expert in global optimization. Professor Žilinskas's research has concentrated on studying models for the objective function, the development and implementation of efficient algorithms for global optimization with single and multiple objectives, and application of algorithms for solving real-world practical problems.

Kernels for Vector-Valued Functions

Author : Mauricio A. Álvarez
Publisher : Foundations & Trends
ISBN 13 : 9781601985583
Total Pages : 86 pages
Book Rating : 4.9/5 (855 download)

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Book Synopsis Kernels for Vector-Valued Functions by : Mauricio A. Álvarez

Download or read book Kernels for Vector-Valued Functions written by Mauricio A. Álvarez and published by Foundations & Trends. This book was released on 2012 with total page 86 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph reviews different methods to design or learn valid kernel functions for multiple outputs, paying particular attention to the connection between probabilistic and regularization methods.

High-dimensional Optimization and Probability

Author :
Publisher :
ISBN 13 : 9788303100832
Total Pages : 0 pages
Book Rating : 4.1/5 (8 download)

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Book Synopsis High-dimensional Optimization and Probability by :

Download or read book High-dimensional Optimization and Probability written by and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Numerical Infinities and Infinitesimals in Optimization

Author : Yaroslav D. Sergeyev
Publisher : Springer Nature
ISBN 13 : 3030936422
Total Pages : 372 pages
Book Rating : 4.0/5 (39 download)

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Book Synopsis Numerical Infinities and Infinitesimals in Optimization by : Yaroslav D. Sergeyev

Download or read book Numerical Infinities and Infinitesimals in Optimization written by Yaroslav D. Sergeyev and published by Springer Nature. This book was released on 2022-07-05 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a friendly introduction to the paradigm and proposes a broad panorama of killing applications of the Infinity Computer in optimization: radically new numerical algorithms, great theoretical insights, efficient software implementations, and interesting practical case studies. This is the first book presenting to the readers interested in optimization the advantages of a recently introduced supercomputing paradigm that allows to numerically work with different infinities and infinitesimals on the Infinity Computer patented in several countries. One of the editors of the book is the creator of the Infinity Computer, and another editor was the first who has started to use it in optimization. Their results were awarded by numerous scientific prizes. This engaging book opens new horizons for researchers, engineers, professors, and students with interests in supercomputing paradigms, optimization, decision making, game theory, and foundations of mathematics and computer science. “Mathematicians have never been comfortable handling infinities... But an entirely new type of mathematics looks set to by-pass the problem... Today, Yaroslav Sergeyev, a mathematician at the University of Calabria in Italy solves this problem... ” MIT Technology Review “These ideas and future hardware prototypes may be productive in all fields of science where infinite and infinitesimal numbers (derivatives, integrals, series, fractals) are used.” A. Adamatzky, Editor-in-Chief of the International Journal of Unconventional Computing. “I am sure that the new approach ... will have a very deep impact both on Mathematics and Computer Science.” D. Trigiante, Computational Management Science. “Within the grossone framework, it becomes feasible to deal computationally with infinite quantities, in a way that is both new (in the sense that previously intractable problems become amenable to computation) and natural”. R. Gangle, G. Caterina, F. Tohmé, Soft Computing. “The computational features offered by the Infinity Computer allow us to dynamically change the accuracy of representation and floating-point operations during the flow of a computation. When suitably implemented, this possibility turns out to be particularly advantageous when solving ill-conditioned problems. In fact, compared with a standard multi-precision arithmetic, here the accuracy is improved only when needed, thus not affecting that much the overall computational effort.” P. Amodio, L. Brugnano, F. Iavernaro & F. Mazzia, Soft Computing

Bayesian Optimization with Application to Computer Experiments

Author : Tony Pourmohamad
Publisher : Springer Nature
ISBN 13 : 3030824586
Total Pages : 113 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Bayesian Optimization with Application to Computer Experiments by : Tony Pourmohamad

Download or read book Bayesian Optimization with Application to Computer Experiments written by Tony Pourmohamad and published by Springer Nature. This book was released on 2021-10-04 with total page 113 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces readers to Bayesian optimization, highlighting advances in the field and showcasing its successful applications to computer experiments. R code is available as online supplementary material for most included examples, so that readers can better comprehend and reproduce methods. Compact and accessible, the volume is broken down into four chapters. Chapter 1 introduces the reader to the topic of computer experiments; it includes a variety of examples across many industries. Chapter 2 focuses on the task of surrogate model building and contains a mix of several different surrogate models that are used in the computer modeling and machine learning communities. Chapter 3 introduces the core concepts of Bayesian optimization and discusses unconstrained optimization. Chapter 4 moves on to constrained optimization, and showcases some of the most novel methods found in the field. This will be a useful companion to researchers and practitioners working with computer experiments and computer modeling. Additionally, readers with a background in machine learning but minimal background in computer experiments will find this book an interesting case study of the applicability of Bayesian optimization outside the realm of machine learning.