4 edition of **Deterministic Global Optimization** found in the catalog.

- 213 Want to read
- 39 Currently reading

Published
**October 1, 1999**
by Springer
.

Written in English

- Applied mathematics,
- Computer Programming,
- Probability & statistics,
- Optimization (Mathematical Theory),
- Mathematics,
- Science/Mathematics,
- Game Theory,
- Linear Programming,
- Mechanics - Dynamics - General,
- Mathematics / Game Theory,
- Mathematics / Linear Programming,
- Mathematics-Linear Programming,
- Science-Mechanics - Dynamics - General,
- Mathematical optimization,
- Nonlinear Programming

The Physical Object | |
---|---|

Format | Hardcover |

Number of Pages | 760 |

ID Numbers | |

Open Library | OL7809055M |

ISBN 10 | 0792360141 |

ISBN 10 | 9780792360148 |

A deterministic global optimization method that is applicable to general nonlinear programming problems composed of twice-differentiable objective and constraint functions is proposed. The method hybridizes the branch-and-bound algorithm and a convex cut function (CCF). For a given subregion, the difference of a convex underestimator that does. This book begins with a concentrated introduction into deterministic global optimization and moves forward to present new original results from the authors who are well known experts in the field. Multiextremal continuous problems that have an unknown structure with Lipschitz objective functions and functions having the first Lipschitz.

This book provides a unified and insightful treatment of deterministic global optimization. It introduces theoretical and algorithmic advances that address the computation and characterization of global optima, determine valid lower and upper bounds on the global minima and maxima, and enclose all solutions of nonlinear constrained systems of equations. Springer is publishing the series “Optimization and Its Applications” aims to publish state-of-the-art expository works (monographs, contributed volumes, textbooks) that focus on algorithms for solving optimization problems and also study applications involving such problems. Some of the topics covered include nonlinear optimization (convex and nonconvex), network flow problems, stochastic.

Provides a unified treatment of deterministic global optimization. This book introduces theoretical and algorithmic advances that address the computation and characterization of global optima, determine valid lower and upper bounds on the global minima and maxima, and enclose all solutions of nonlinear constrained systems of equations. BOOK REPORTS Deterministic Global Optimization: Theory, Methods and Applications. By Christodoulos A. Floudas. Kluwer Academic, Dordrecht. (). pages. $

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The primary goal of this book is three fold: first, to introduce the reader to the basics of deterministic global optimization; second, to present important theoretical and algorithmic advances for several classes of mathematical prob lems that include biconvex and bilinear; problems, signomial problems, general twice differentiable Cited by: This book begins with a concentrated introduction into deterministic global optimization and moves forward to present new original results from the authors who are well known experts in the field.

Multiextremal continuous problems that have an unknown structure with Lipschitz objective functions and functions having the first Lipschitz derivatives defined over hyperintervals are by: The area of Deterministic Global Optimization introduces theoretical, algorithmic and computational ad vances that (i) address the computation and characterization of global minima and maxima, (ii) determine valid lower and upper bounds on the global minima and maxima, and (iii) address the enclosure of all solutions of nonlinear con strained systems of equations.

This book begins with a concentrated introduction into deterministic global optimization and moves forward to present new original results from the authors who are well known experts in the field.

Multiextremal continuous problems that have an unknown structure with Lipschitz objective functions and functions having the first Lipschitz derivatives defined over hyperintervals are examined.

Advances in Stochastic and Deterministic Global Optimization (Springer Optimization and Its Applications Book ) - Kindle edition by Pardalos, Panos M., Zhigljavsky, Anatoly, Žilinskas, Julius.

Download it once and read it on your Kindle device, PC, phones or tablets. Use features like bookmarks, note taking and highlighting while reading Advances in Stochastic and Deterministic Global Manufacturer: Springer.

The area of Deterministic Global Optimization introduces theoretical, algorithmic and computational ad vances that (i) address the computation and characterization of global minima and maxima, (ii) determine valid lower and upper bounds on the global minima and maxima, and (iii) address the enclosure of all solutions of nonlinear con strained systems of equations.4/5(1).

This monograph deals with a general class of solution approaches in deterministic global optimization, namely the geometric branch-and-bound methods which are popular algorithms, for instance, in Lipschitzian optimization, d.c. programming, and interval also introduces a new concept for the rate of convergence and analyzes several bounding operations reported in the literature, from the.

The primary goal of this book is three fold: first, to introduce the reader to the basics of deterministic global optimization; second, to present important theoretical and algorithmic advances for several classes of mathematical prob lems that include biconvex and bilinear; problems, signomial problems, general twice differentiable.

Deterministic global optimization. Dordrecht/Boston/London: Kluwer Academic Publishers. Search within book. Type for suggestions. Table of contents Previous. Page About this reference work. Introduction. Editors and affiliations.

Bibliographic information. Global Models. Global Optimization. Global Solution. Goal Constraints. Goal. This monograph deals with a general class of solution approaches in deterministic global optimization, namely the geometric branch-and-bound methods which are popular algorithms, for instance, in Lipschitzian optimization, d.c.

programming, and interval also introduces a new concept for the rate of convergence and analyzes several bounding operations reported in the literature, from the Cited by: Deterministic Global Optimization: Geometric Branch-and-bound Methods and their Applications (Springer Optimization and Its Applications Book 63) - Kindle edition by Scholz, Daniel.

Download it once and read it on your Kindle device, PC, phones or cturer: Springer. This monograph deals with a general class of solution approaches in deterministic global optimization, namely the geometric branch-and-bound methods which are popular algorithms, for instance, in Lipschitzian optimization, d.c.

programming, and interval also introduces a new concept for the rate of convergence and analyzes several bounding operations reported in the literature.

This book begins with a concentrated introduction into deterministic global optimization and moves forward to present new original results from the authors who are well known experts in the field. As a consequence, we are seeing the creation of a large and increasing number of diverse algorithms for solving a wide variety of multiextremal global optimization problems.

The goal of this book is to systematically clarify and unify these diverse approaches in order to provide insight into the underlying concepts and their pro perties. 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. Deterministic Global Optimization: An Introduction to the Diagonal Approach Yaroslav D.

Sergeyev, Dmitri E. Kvasov (auth.) This book begins with a concentrated introduction into deterministic global optimization and moves forward to present new original results from the authors who are well known experts in the field.

Deterministic global optimization is a branch of numerical optimization which focuses on finding the global solutions of an optimization problem whilst providing theoretical guarantees that the reported solution is indeed the global one, within some predefined tolerance.

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. This book begins with a concentrated introduction into deterministic global optimization and moves forward to present new original results from the authors who are well known experts in the : Yaroslav D.

Sergeyev. Deterministic Global Optimization by Yaroslav D. Sergeyev OverDrive (Rakuten OverDrive): eBooks, audiobooks and videos for libraries This book begins with a concentrated introduction into deterministic global optimization and moves forward to present new original results from the authors who are well known experts in the field.

As a consequence, we are seeing the creation of a large and increasing number of diverse algorithms for solving a wide variety of multiextremal global optimization problems. The goal of this book is to systematically clarify and unify these diverse approaches in order to provide insight into the underlying concepts and their pro perties.2/5(1).Deterministic Optimization and Design Jay R.

Lund UC Davis Fall 3 Some Thoughts on Optimization "All models are wrong, but some are useful." - G.E.P. Box (), Robustness in Statistics. "Operations research is the art of giving bad answers to problems to which otherwise worse answers are .Global optimization is a branch of applied mathematics and numerical analysis that attempts to find the global minima or maxima of a function or a set of functions on a given set.

It is usually described as a minimization problem because the maximization of the real-valued function () is obviously equivalent to the minimization of the function ():= (−) ⋅ ().