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High Performance Optimization
For a long time the techniques of solving linear optimization (LP) problems improved only marginally. Fifteen years ago, however, a revolutionary discovery changed everything. A new `golden age' for optimization started, which is continuing up to the current time. What is the cause of the excitement? Techniques of linear programming formed previously an isolated body of knowledge. Then suddenly a tunnel was built linking it with a rich and promising land, part of which was already cultivated, part of which was completely unexplored. These revolutionary new techniques are now applied to solve conic linear problems. This makes it possible to model and solve large classes of essentially nonlinear optimization problems as efficiently as LP problems. This volume gives an overview of the latest developments of such `High Performance Optimization Techniques'. The first part is a thorough treatment of interior point methods for semidefinite programming problems. The second part reviews today's most exciting research topics and results in the area of convex optimization. Audience: This volume is for graduate students and researchers who are interested in modern optimization techniques.
Ethnic Minorities and Dutch as a Second Language
Language acquisition is a human endeavor par excellence. As children, all human beings learn to understand and speak at least one language: their mother tongue. It is a process that seems to take place without any obvious effort. Second language learning, particularly among adults, causes more difficulty. The purpose of this series is to compile a collection of high-quality monographs on language acquisition. The series serves the needs of everyone who wants to know more about the problem of language acquisition in general and/or about language acquisition in specific contexts.
Advances in Sensitivity Analysis and Parametric Programming
The standard view of Operations Research/Management Science (OR/MS) dichotomizes the field into deterministic and probabilistic (nondeterministic, stochastic) subfields. This division can be seen by reading the contents page of just about any OR/MS textbook. The mathematical models that help to define OR/MS are usually presented in terms of one subfield or the other. This separation comes about somewhat artificially: academic courses are conveniently subdivided with respect to prerequisites; an initial overview of OR/MS can be presented without requiring knowledge of probability and statistics; text books are conveniently divided into two related semester courses, with deterministic models coming first; academics tend to specialize in one subfield or the other; and practitioners also tend to be expert in a single subfield. But, no matter who is involved in an OR/MS modeling situation (deterministic or probabilistic - academic or practitioner), it is clear that a proper and correct treatment of any problem situation is accomplished only when the analysis cuts across this dichotomy.
Aspects of Semidefinite Programming
Semidefinite programming has been described as linear programming for the year 2000. It is an exciting new branch of mathematical programming, due to important applications in control theory, combinatorial optimization and other fields. Moreover, the successful interior point algorithms for linear programming can be extended to semidefinite programming. In this monograph the basic theory of interior point algorithms is explained. This includes the latest results on the properties of the central path as well as the analysis of the most important classes of algorithms. Several "classic" applications of semidefinite programming are also described in detail. These include the Lovász theta function and the MAX-CUT approximation algorithm by Goemans and Williamson. Audience: Researchers or graduate students in optimization or related fields, who wish to learn more about the theory and applications of semidefinite programming.
Chess Competitions, 1971-2010
This comprehensive reference work presents detailed bibliographical information about chess publications--books, bulletins and programs--covering competitions held around the world from 1971 through 2010. It catalogs 3,895 entries tracked through 5,381 items with many cross-references. Information for each entry includes year and country of publication, sponsors, publisher, editors, language, alternate titles, mergers and source. An index of competitions is included.
Numerical Analysis and Optimization
Presenting the latest findings in the field of numerical analysis and optimization, this volume balances pure research with practical applications of the subject. Accompanied by detailed tables, figures, and examinations of useful software tools, this volume will equip the reader to perform detailed and layered analysis of complex datasets. Many real-world complex problems can be formulated as optimization tasks. Such problems can be characterized as large scale, unconstrained, constrained, non-convex, non-differentiable, and discontinuous, and therefore require adequate computational methods, algorithms, and software tools. These same tools are often employed by researchers working in curre...
Handbook of Semidefinite Programming
Semidefinite programming (SDP) is one of the most exciting and active research areas in optimization. It has and continues to attract researchers with very diverse backgrounds, including experts in convex programming, linear algebra, numerical optimization, combinatorial optimization, control theory, and statistics. This tremendous research activity has been prompted by the discovery of important applications in combinatorial optimization and control theory, the development of efficient interior-point algorithms for solving SDP problems, and the depth and elegance of the underlying optimization theory. The Handbook of Semidefinite Programming offers an advanced and broad overview of the current state of the field. It contains nineteen chapters written by the leading experts on the subject. The chapters are organized in three parts: Theory, Algorithms, and Applications and Extensions.
Combinatorial and Algorithmic Mathematics
This book provides an insightful and modern treatment of combinatorial and algorithmic mathematics, with an elegant transition from mathematical foundations to optimization. It is designed for mathematics, computer science, and engineering students. The book is crowned with modern optimization methodologies. Without the optimization part, the book can be used as a textbook in a one- or two-term undergraduate course in combinatorial and algorithmic mathematics. The optimization part can be used in a one-term high-level undergraduate course, or a low- to medium-level graduate course. The book spans xv+527 pages across 12 chapters, featuring 391 LaTeX pictures, 108 tables, and 218 illustrative examples. There are also 159 nontrivial exercises included at the end of the chapters, with complete solutions included at the end of the book. Complexity progressively grows, building upon previously introduced concepts. The book includes traditional topics as well as cutting-edge topics in modern optimization.
Interior Point Methods for Linear Optimization
The era of interior point methods (IPMs) was initiated by N. Karmarkar’s 1984 paper, which triggered turbulent research and reshaped almost all areas of optimization theory and computational practice. This book offers comprehensive coverage of IPMs. It details the main results of more than a decade of IPM research. Numerous exercises are provided to aid in understanding the material.
Operations Research for Social Good
Advance your knowledge of operations research and social good! Recent technological developments allow data analytics practitioners to solve large problems better and faster with state-of-the-art artificial intelligence (AI) tools. At the same time, humanity faces overarching challenges such as the climate crisis, child malnutrition, systemic racism, and global pandemics, among others. Operations Research for Social Good: A Practitioner’s Introduction Using SAS and Python showcases operations research (OR) methodologies typically required in engineering curricula to applications targeted to make this world a better place. Designed for data scientists, analytics and operations research practitioners, and graduate-level students interested in learning optimization modeling with applied use cases, this book provides the skills to model and solve OR problems with both SAS and Python as well as practical tools and tips to bridge the gap between academic learning and real-world implementations based on Data4Good initiatives.
