Alexander Schrijver

<P>An in-depth overview of polyhedral methods and efficient algorithms in combinatorial optimization. These methods form a broad, coherent and powerful kernel in combinatorial optimization, with strong links to discrete mathematics, mathematical programming and computer science. In eight parts, various areas are treated, each starting with an elementary introduction to the area, with short, elegant proofs of the principal results, and each evolving to the more advanced methods and results, with full proofs of some of the deepest theorems in the area. Over 4000 references to further research are given, and historical surveys on the basic subjects are presented.</P>

内容紹介

<P>Aus den Rezensionen: </P> <P></P> <P>"… ‘Effiziente Algorithmen‘ und ‘Kombinatorik der Polyeder‘ bilden ... den roten Faden durch das monumentale Werk ... Sehr knappe Einführungen in das jeweilige Thema, ein paar einfache erste Resultate ... und dann eine enzykoplädische [sic] Durchsicht der Methoden, Ideen, Varianten, Laufzeiten, mit einer überaus detaillierten Literaturliste und historischen Zusammenfassung. Alles wird nüchtern … dargeboten … Man blättert das Buch durch … und erkennt mit Respekt und Staunen, dass der Autor alles, aber auch wirklich alles durchgearbeitet hat - eine große Leistung. … Ich kenne kein vergleichbares neueres Werk …" (M. Aigner, 2006, Issue 3, S. 15-16)</P>

レビュー

<P>Alexander Schrijver is one of the most respected researchers in this area. He has won the Dantzig award, the Fulkerson prize (twice) and the Lanchester Prize for his earlier classic text on "Theory of Linear and Integer Programming". </P>

著者について

From the reviews: "About 30 years ago, when I was a student, the first book on combinatorial optimization came out referred to as "the Lawler" simply. I think that now, with this volume Springer has landed a coup: "The Schrijver". The box is offered for less than 90.- EURO, which to my opinion is one of the best deals after the introduction of this currency." OR-Spectrum

内容詳細

Combinatorial Optimization (Algorithms and Combinatorics)

Frédéric Chazal

Vin de Silva

Marc Glisse

Steve Oudot

This book is a comprehensive treatment of the theory of persistence modules over the real line. It presents a set of mathematical tools to analyse the structure and to establish the stability of such modules, providing a sound mathematical framework for the study of persistence diagrams. Completely self-contained, this brief introduces the notion of persistence measure and makes extensive use of a new calculus of quiver representations to facilitate explicit computations. Appealing to both beginners and experts in the subject, The Structure and Stability of Persistence Modules provides a purely algebraic presentation of persistence, and thus complements the existing literature, which focuses mainly on topological and algorithmic aspects.

The Structure and Stability of Persistence Modules (SpringerBriefs in Mathematics)

Saunders MacLane

Ieke Moerdijk

An introduction to the theory of toposes which begins with illustrative examples and goes on to explain the underlying ideas of topology and sheaf theory as well as the general theory of elementary toposes and geometric morphisms and their relation to logic.

Sheaves arose in geometry as coefficients for cohomology and as descriptions of the functions appropriate to various kinds of manifolds. Sheaves also appear in logic as carriers for models of set theory. This text presents topos theory as it has developed from the study of sheaves. Beginning with several examples, it explains the underlying ideas of topology and sheaf theory as well as the general theory of elementary toposes and geometric morphisms and their relation to logic.

Sheaves in Geometry and Logic: A First Introduction to Topos Theory (Universitext)

Saunders Mac Lane

An array of general ideas useful in a wide variety of fields. Starting from the foundations, this book illuminates the concepts of category, functor, natural transformation, and duality. It then turns to adjoint functors, which provide a description of universal constructions, an analysis of the representations of functors by sets of morphisms, and a means of manipulating direct and inverse limits. These categorical concepts are extensively illustrated in the remaining chapters, which include many applications of the basic existence theorem for adjoint functors. The categories of algebraic systems are constructed from certain adjoint-like data and characterised by Beck's theorem. After considering a variety of applications, the book continues with the construction and exploitation of Kan extensions. This second edition includes a number of revisions and additions, including new chapters on topics of active interest: symmetric monoidal categories and braided monoidal categories, and the coherence theorems for them, as well as 2-categories and the higher dimensional categories which have recently come into prominence.

Categories for the Working Mathematician begins with foundations, illuminating concepts such as category, functor, natural transformation, and duality. It then continues by extensively illustrating these categorical concepts while presenting applications to more advanced topics. This second edition includes many revisions and additions.

Categories for the Working Mathematician (Graduate Texts in Mathematics)

Stefan Wagner

Software Product Quality Control (English Edition)

Christopher M. Bishop

This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

The field of pattern recognition has undergone substantial development over the years. This book reflects these developments while providing a grounding in the basic concepts of pattern recognition and machine learning. It is aimed at advanced undergraduates or first year PhD students, as well as researchers and practitioners.

ここ10年で劇的に進んだ機械学習の実用化の裏には、基礎的なアルゴリズムやテクニックに関する数々の重要な発展がある。例えば、限られた専門家のものだったベイズ手法はいまや主流となり、確率論的技術を説明し、応用するための一般的な枠組みとして、数々のグラフィックモデルが登場している。ベイズ手法の実用可能性は、変分ベイズや期待伝播といった幅広い近似的推論の発展によりおおいに高まり、カーネルに基づく新しいモデルは、アルゴリズムと応用に大きな影響を与えている。このまったく新しいテキストは、そうした最近の発展を考察しながら、パターン認識と機械学習という分野を総合的に紹介している。本書の対象となるのは、上級の大学生、博士課程1年目の学生、研究者、実務に携わる人など。パターン認識や機械学習に関する概念の予備知識は必要ない。ただし、多変量解析や基本的な線形代数を熟知していることが求められる。また、確率に関する経験があれば理解に役立つが、確率論の基本は本書でも簡単に説明されているので、絶対に必要というわけではない。機械学習、統計、コンピュータ科学、信号処理、コンピュータビジョン、データマインニング、生物情報学などの講座のテキストとしても使える。講座を担当する指導者のために、難易度別にランク付けした400問以上の練習問題をはじめ、幅広い教材も用意されている。練習問題の解答例は、一部を本書のWebサイトから、それ以外は指導者の求めに応じて出版社から入手できる。追加教材も充実しており、読者はWebサイトで最新情報を自由に閲覧できる。近日発売の姉妹書では、パターン認識と機械学習の実用面を扱っており、主要アルゴリズムの無料ソフトウェア実行、データ例セット、デモンストレーションプログラムが盛り込まれている。クリストファー・ビショップは、マイクロソフトリサーチ・ケンブリッジのアシスタントディレクターで、エジンバラ大学でコンピュータ科学の教授も務めている。ケンブリッジのダーウィンカレッジの研究員でもあり、英国王立工学アカデミーの研究員にも先ごろ選出された。著書『Neural Networks for Pattern Recognition』はテキストとして広く採用されている。

商品説明

<P>This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.</P>

<b>​</b><b>Chris Bishop</b> is a Microsoft Distinguished Scientist and the Laboratory Director at Microsoft Research Cambridge. He is also Professor of Computer Science at the University of Edinburgh, and a Fellow of Darwin College, Cambridge. In 2004, he was elected Fellow of the Royal Academy of Engineering, and in 2007 he was elected Fellow of the Royal Society of Edinburgh. <div><br/></div><div>Chris obtained a BA in Physics from Oxford, and a PhD in Theoretical Physics from the University of Edinburgh, with a thesis on quantum field theory. He then joined Culham Laboratory where he worked on the theory of magnetically confined plasmas as part of the European controlled fusion programme.</div>

Pattern Recognition and Machine Learning (Information Science and Statistics)

Stuart Coles

Directly oriented towards real practical application, this book develops both the basic theoretical framework of extreme value models and the statistical inferential techniques for using these models in practice. Intended for statisticians and non-statisticians alike, the theoretical treatment is elementary, with heuristics often replacing detailed mathematical proof. Most aspects of extreme modeling techniques are covered, including historical techniques (still widely used) and contemporary techniques based on point process models. A wide range of worked examples, using genuine datasets, illustrate the various modeling procedures and a concluding chapter provides a brief introduction to a number of more advanced topics, including Bayesian inference and spatial extremes. All the computations are carried out using S-PLUS, and the corresponding datasets and functions are available via the Internet for readers to recreate examples for themselves. An essential reference for students and researchers in statistics and disciplines such as engineering, finance and environmental science, this book will also appeal to practitioners looking for practical help in solving real problems. Stuart Coles is Reader in Statistics at the University of Bristol, UK, having previously lectured at the universities of Nottingham and Lancaster. In 1992 he was the first recipient of the Royal Statistical Society's research prize. He has published widely in the statistical literature, principally in the area of extreme value modeling.

An Introduction to Statistical Modeling of Extreme Values (Springer Series in Statistics)

Yves Bertot

Pierre Castéran

G. Huet

C. Paulin-Mohring

A practical introduction to the development of proofs and certified programs using Coq. An invaluable tool for researchers, students, and engineers interested in formal methods and the development of zero-fault software.

Interactive Theorem Proving and Program Development: Coq’Art: The Calculus of Inductive Constructions (Texts in Theoretical Computer Science. An EATCS Series)

Masaru Tomita

Parsing Efficiency is crucial when building practical natural language systems. 'Ibis is especially the case for interactive systems such as natural language database access, interfaces to expert systems and interactive machine translation. Despite its importance, parsing efficiency has received little attention in the area of natural language processing. In the areas of compiler design and theoretical computer science, on the other hand, parsing algorithms 3 have been evaluated primarily in terms of the theoretical worst case analysis (e.g. lXn», and very few practical comparisons have been made. This book introduces a context-free parsing algorithm that parses natural language more efficiently than any other existing parsing algorithms in practice. Its feasibility for use in practical systems is being proven in its application to Japanese language interface at Carnegie Group Inc., and to the continuous speech recognition project at Carnegie-Mellon University. This work was done while I was pursuing a Ph.D degree at Carnegie-Mellon University. My advisers, Herb Simon and Jaime Carbonell, deserve many thanks for their unfailing support, advice and encouragement during my graduate studies. I would like to thank Phil Hayes and Ralph Grishman for their helpful comments and criticism that in many ways improved the quality of this book. I wish also to thank Steven Brooks for insightful comments on theoretical aspects of the book (chapter 4, appendices A, B and C), and Rich Thomason for improving the linguistic part of tile book (the very beginning of section 1.1).

Efficient Parsing for Natural Language: A Fast Algorithm for Practical Systems (The Springer International Series in Engineering and Computer Science)

Sivarama P. Dandamudi

Introduces Linux concepts to programmers who are familiar with other operating systems such as Windows XP Provides comprehensive coverage of the Pentium assembly language

Guide to Assembly Language Programming in Linux

Marcus Hutter

Personal motivation. The dream of creating artificial devices that reach or outperform human inteUigence is an old one. It is also one of the dreams of my youth, which have never left me. What makes this challenge so interesting? A solution would have enormous implications on our society, and there are reasons to believe that the AI problem can be solved in my expected lifetime. So, it's worth sticking to it for a lifetime, even if it takes 30 years or so to reap the benefits. The AI problem. The science of artificial intelligence (AI) may be defined as the construction of intelligent systems and their analysis. A natural definition of a system is anything that has an input and an output stream. Intelligence is more complicated. It can have many faces like creativity, solving prob lems, pattern recognition, classification, learning, induction, deduction, build ing analogies, optimization, surviving in an environment, language processing, and knowledge. A formal definition incorporating every aspect of intelligence, however, seems difficult. Most, if not all known facets of intelligence can be formulated as goal driven or, more precisely, as maximizing some utility func tion. It is, therefore, sufficient to study goal-driven AI; e. g. the (biological) goal of animals and humans is to survive and spread. The goal of AI systems should be to be useful to humans.

Universal Artificial Intelligence: Sequential Decisions Based on Algorithmic Probability (Texts in Theoretical Computer Science. An EATCS Series)

Artur S. D'avila Garcez

Computing Science and Artificial Intelligence are concerned with producing devices that help and/or replace human beings in their daily activities. To be successful, adequate modelling of these activities needs to be carried out and this has accelerated the development of both old and new disciplines, including Logic and Computation, Neural Networks, Genetic Algorithms and Probabilistic/Casual Networks. This book looks at how these techniques could complement each other and how, by understanding the role of each in a particular application, we can pave the way towards the development of more effective intelligent systems.

Neural-Symbolic Learning Systems: Foundations And Applications (Perspectives in Neural Computing)

Anthony Lawrence

A description of the inertial technology used for guidance, control, and navigation, discussing in detail the principles, operation, and design of sensors, gyroscopes, and accelerometers, as well as the advantages and disadvantages of particular systems. An engineer with long practical experience in the field, the author elucidates such recent developments as fibre-optic gyroscopes, solid-state accelerometers, and the global positioning system. This will be of interest to researchers and practising engineers involved in systems engineering, aeronautics, space research, and navigation on both land and sea.

Modern Inertial Technology: Navigation, Guidance, and Control (Mechanical Engineering Series)

Shun-ichi Amari

This is the first comprehensive book on information geometry, written by the founder of the field. It begins with an elementary introduction to dualistic geometry and proceeds to a wide range of applications, covering information science, engineering, and neuroscience. It consists of four parts, which on the whole can be read independently. A manifold with a divergence function is first introduced, leading directly to dualistic structure, the heart of information geometry. This part (Part I) can be apprehended without any knowledge of differential geometry. An intuitive explanation of modern differential geometry then follows in Part II, although the book is for the most part understandable without modern differential geometry. Information geometry of statistical inference, including time series analysis and semiparametric estimation (the Neyman–Scott problem), is demonstrated concisely in Part III. Applications addressed in Part IV include hot current topics in machine learning, signal processing, optimization, and neural networks. The book is interdisciplinary, connecting mathematics, information sciences, physics, and neurosciences, inviting readers to a new world of information and geometry. This book is highly recommended to graduate students and researchers who seek new mathematical methods and tools useful in their own fields.

Information Geometry and Its Applications (Applied Mathematical Sciences)

Elsayed Azzouz

A.K. Nandi

Automatic modulation recognition is a rapidly evolving area of signal analysis. In recent years, interest from the academic and military research institutes has focused around the research and development of modulation recognition algorithms. Any communication intelligence (COMINT) system comprises three main blocks: receiver front-end, modulation recogniser and output stage. Considerable work has been done in the area of receiver front-ends. The work at the output stage is concerned with information extraction, recording and exploitation and begins with signal demodulation, that requires accurate knowledge about the signal modulation type. There are, however, two main reasons for knowing the current modulation type of a signal; to preserve the signal information content and to decide upon the suitable counter action, such as jamming. Automatic Modulation Recognition of Communications Signals describes in depth this modulation recognition process. Drawing on several years of research, the authors provide a critical review of automatic modulation recognition. This includes techniques for recognising digitally modulated signals. The book also gives comprehensive treatment of using artificial neural networks for recognising modulation types. Automatic Modulation Recognition of Communications Signals is the first comprehensive book on automatic modulation recognition. It is essential reading for researchers and practising engineers in the field. It is also a valuable text for an advanced course on the subject.

Automatic Modulation Recognition of Communication Signals

Janine Bennett

Fabien Vivodtzev

Valerio Pascucci

This book contains papers presented at the Workshop on the Analysis of Large-scale, High-Dimensional, and Multi-Variate Data Using Topology and Statistics, held in Le Barp, France, June 2013. It features the work of some of the most prominent and recognized leaders in the field who examine challenges as well as detail solutions to the analysis of extreme scale data. The book presents new methods that leverage the mutual strengths of both topological and statistical techniques to support the management, analysis, and visualization of complex data. It covers both theory and application and provides readers with an overview of important key concepts and the latest research trends. Coverage in the book includes multi-variate and/or high-dimensional analysis techniques, feature-based statistical methods, combinatorial algorithms, scalable statistics algorithms, scalar and vector field topology, and multi-scale representations. In addition, the book details algorithms that are broadly applicable and can be used by application scientists to glean insight from a wide range of complex data sets.

Topological and Statistical Methods for Complex Data: Tackling Large-Scale, High-Dimensional, and Multivariate Data Spaces (Mathematics and Visualization)

Bertrand Meyer

This text combines a practical, hands-on approach to programming with the introduction of sound theoretical support focused on teaching the construction of high-quality software. A major feature of the book is the use of Design by Contract.

Touch of Class: Learning to Program Well with Objects and Contracts

Jorge Nocedal

Stephen Wright

Optimization is an important tool used in decision science and for the analysis of physical systems used in engineering. One can trace its roots to the Calculus of Variations and the work of Euler and Lagrange. This natural and reasonable approach to mathematical programming covers numerical methods for finite-dimensional optimization problems. It begins with very simple ideas progressing through more complicated concepts, concentrating on methods for both unconstrained and constrained optimization.

Numerical Optimization (Springer Series in Operations Research and Financial Engineering)

Peter Eris Kloeden

Eckhard Platen

Henri Schurz

The rich set of examples and problems integrated throughout this book will help readers gain a better understanding of where and how to apply Statistical Quality Control (SQC) tools. Real projects, cases and data sets show clearly how SQC tools are used in practice. Topics are covered in the right amount of detail to give insight into their relative importance in modern quality assurance and the ability to immediately use them.

This book provides an easily accessible, computationally-oriented introduction into the numerical solution of stochastic differential equations using computer experiments. It develops in the reader an ability to apply numerical methods solving stochastic differential equations. It also creates an intuitive understanding of the necessary theoretical background. Software containing programs for over 100 problems is available online.

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