الصفحة 191
الصفحة 191
Algorithms on Trees and Graphs : With Python Code
Introduces graph algorithms on an intuitive basis followed by a detailed exposition using structured pseudocode, with correctness proofs as well as worst-case analyses. Centered around the fundamental issue of graph isomorphism, the content goes beyond classical graph problems of shortest paths, spanning trees, flows in networks, and matchings in bipartite graphs. Advanced algorithmic results and techniques of practical relevance are presented in a coherent and consolidated way. Numerous illustrations, examples, problems, exercises, and a comprehensive bibliography support students and professionals in using the book as a text and source of reference. Furthermore, Python code for all algorithms presented is given in an appendix. Topics and features: Algorithms are first presented on an intuitive basis, followed by a detailed exposition using structured pseudocode / Correctness proofs are given, together with a worst-case analysis of the algorithms / Full implementation of all the algorithms in Python / An extensive chapter is devoted to the algorithmic techniques used in the book / Solutions to all the problems
Algorithms in Invariant Theory
The book of Sturmfels is both an easy-to-read textbook for invariant theory and a challenging research monograph that introduces a new approach to the algorithmic side of invariant theory. The Groebner bases method is the main tool by which the central problems in invariant theory become amenable to algorithmic solutions.
Algorithms in Bioinformatics : Theory and Implementation
Explores a comprehensive and insightful treatment of the practical application of bioinformatic algorithms in a variety of fields. Delivers a fulsome treatment of some of the main algorithms used to explain biological functions and relationships. It introduces readers to the art of algorithms in a practical manner which is linked with biological theory and interpretation. The book covers many key areas of bioinformatics, including global and local sequence alignment, forced alignment, detection of motifs, Sequence logos, Markov chains or information entropy. Other novel approaches are also described, such as Self-Sequence alignment, Objective Digital Stains (ODSs) or Spectral Forecast and the Discrete Probability Detector (DPD) algorithm. Readers will also benefit from the inclusion of: A detailed presentation of new methods, such as Self-sequence alignment, Objective Digital Stains and Spectral Forecast ; A treatment of sequence alignment, including local sequence alignment, global sequence alignment and forced sequence alignment with full implementations ; Discussions of position-specific weight matrices, including the count, weight, relative frequencies, and log-likelihoods matrices ; A detailed presentation of the methods related to Markov Chains as well as a description of their implementation in Bioinformatics and adjacent fields ; An examination of information and entropy, including sequence logos and explanations related to their meaning ; A chapter on philosophical transactions that allows the reader a broader view of the prediction process ; Extensive worked examples with detailed case studies that point out the meaning of different results
Algorithms for Decision Making
A broad introduction to algorithms for decision making under uncertainty, introducing the underlying mathematical problem formulations and the algorithms for solving them. Automated decision-making systems or decision-support systems—used in applications that range from aircraft collision avoidance to breast cancer screening—must be designed to account for various sources of uncertainty while carefully balancing multiple objectives. This textbook provides a broad introduction to algorithms for decision making under uncertainty, covering the underlying mathematical problem formulations and the algorithms for solving them.
Algorithms and data structures for massive datasets
Learn: Probabilistic sketching data structures for practical problems Choosing the right database engine for your application Evaluating and designing efficient on-disk data structures and algorithms Understanding the algorithmic trade-offs involved in massive-scale systems Deriving basic statistics from streaming data Correctly sampling streaming data Computing percentiles with limited space resources Algorithms and Data Structures for Massive Datasets reveals a toolbox of new methods that are perfect for handling modern big data applications. You'll explore the novel data structures and algorithms that underpin Google, Facebook, and other enterprise applications that work with truly massive amounts of data. These effective techniques can be applied to any discipline, from finance to text analysis. Graphics, illustrations, and hands-on industry examples make complex ideas practical to implement in your projects--and there's no mathematical proofs to puzzle over. Work through this one-of-a-kind guide, and you'll find the sweet spot of saving space without sacrificing your data's accuracy. About the Technology Standard algorithms and data structures may become slow--or fail altogether--when applied to large distributed datasets. Choosing algorithms designed for big data saves time, increases accuracy, and reduces processing cost.
Algorithms and data structures : The Basic Toolbox
This book is a concise introduction addressed to students and professionals familiar with programming and basic mathematical language.
Algorithmic learning theory ; Vol. 3734 ; 16th international conference, ALT 2005, Singapore, October 8-11, 2005, Proceedings
This volume contains the papers presented at the 16th Annual InternationalConference on Algorithmic Learning Theory (ALT 2005), which was held (Republic of Singapore), 2005. The main objective of theconference is to provide an interdisciplinary forum for the discussion of the the-oretical foundations of machine learning as well as their relevance to practicalapplications. The volume includes 30 technical contributions, which were selected by theprogram committee from 98 submissions.
Algorithmic learning theory ; 18th International conference, ALT 2007, Sendai, Japan, October 1-4, 2007, Proceedings
This volume contains the papers presented at the 18th International Conf- ence on Algorithmic Learning Theory.The main objective of the conference was to provide an interdisciplinary forum for high-quality talks with a strong theore- cal background and scientiبهc interchange in areas such as query models, on-line learning, inductive inference, algorithmic forecasting, boosting, support vector machines, kernel methods, complexity and learning, reinforcement learning, - supervised learning and grammatical inference.
Algorithmic information theory : Mathematics of digital information processing
This book treats the Mathematics of many important areas in digital information processing.It covers, in a unified presentation, five topics: Data Compression, Cryptography, Sampling (Signal Theory), Error Control Codes, Data Reduction. The thematic choices are practice-oriented. So, the important final part of the book deals with the Discrete Cosine Transform and the Discrete Wavelet Transform, acting in image compression. The presentation is dense, the examples and numerous exercises are concrete. The pedagogic architecture follows increasing mathematical complexity.
Algorithmic Aspects of Bioinformatics
Advances in bioinformatics and systems biology require improved computational methods for analyzing data, while progress in molecular biology is in turn influencing the development of computer science methods. This book introduces some key problems in bioinformatics, discusses the models used to formally describe these problems, and analyzes the algorithmic approaches used to solve them. After introducing the basics of molecular biology and algorithmics, Part I explains string algorithms and alignments; Part II details the field of physical mapping and DNA sequencing; and Part III examines the application of algorithmics to the analysis of biological data. Exciting application examples include predicting the spatial structure of proteins, and computing haplotypes from genotype data. This book describes topics in detail and presents formal models in a mathematically precise, yet intuitive manner, with many figures and chapter summaries, detailed derivations, and examples. It is well suited as an introduction into the field of bioinformatics, and will benefit students and lecturers in bioinformatics and algorithmics, while also offering practitioners an update on current research topics.
Algèbre commutative, Chapitre 10 = Commutative Algebra, Chapter 10
Depth, Regularity, Duality The Mathematics Elements of Nicolas BOURBAKI aim to provide a rigorous, systematic presentation without prerequisites of mathematics from their foundations. This volume of the Book of Commutative Algebra, Book 7 of the treatise, is a continuation of the earlier chapters. It introduces in particular the notions of depth and smoothness, fundamental in algebraic geometry. It ends with the introduction of the dualizing modules and the Grothendieck duality.
Algèbre commutative : Chapitres 5 à 7 = Commutative algebra : Chapters 5 to 7
The Mathematics Elements of Nicolas BOURBAKI aim to provide a rigorous, systematic presentation without prerequisites of mathematics from their foundations.This second volume of the Book of Commutative Algebra, Seventh Book of the treatise, introduces two fundamental notions in commutative algebra, that of algebraic integer and that of valuation, which have many applications in number theory and algebraic geometry.
Algebras, Rings and Modules ; Vol.2
This book provides both the classical aspects of the theory of groups and their representations as well as a general introduction to the modern theory of representations including the representations of quivers and finite partially ordered sets and their applications to finite dimensional algebras.
Algebraic Methods for Nonlinear Control Systems
A self-contained introduction to algebraic control for nonlinear systems suitable for researchers and graduate students.The most popular treatment of control for nonlinear systems is from the viewpoint of differential geometry yet this approach proves not to be the most natural when considering problems like dynamic feedback and realization. Professors Conte, Moog and Perdon develop an alternative linear-algebraic strategy based on the use of vector spaces over suitable fields of nonlinear functions. This algebraic perspective is complementary to, and parallel in concept with, its more celebrated differential-geometric counterpart.Algebraic Methods for Nonlinear Control Systems describes a wide range of results, some of which can be derived using differential geometry but many of which cannot.
Algebraic informatics ; 2nd International conference, CAI 2007, Thessalonkik, Greece, May 21-25, 2007, Revised Selected and Invited Papers
It covers algebraic semantics on graphs and trees, formal power series, syntactic objects, algebraic picture processing, infinite computation, acceptors and transducers for strings, trees, graphs, arrays, etc., and decision problems.
Algebraic Geometry : An Introduction
The book starts with easily-formulated problems with non-trivial solutions – for example, Bézout’s theorem and the problem of rational curves – and uses these problems to introduce the fundamental tools of modern algebraic geometry: dimension; singularities; sheaves; varieties; and cohomology. The treatment uses as little commutative algebra as possible by quoting without proof (or proving only in special cases) theorems whose proof is not necessary in practice, the priority being to develop an understanding of the phenomena rather than a mastery of the technique. A range of exercises is provided for each topic discussed, and a selection of problems and exam papers are collected in an appendix to provide material for further study.
Algebraic and Proof-theoretic Aspects of Non-classical Logics : Papers in Honor of Daniele Mundici on the Occasion of His 60th Birthday
It profound connections between logic and such diverse fields of research as functional analysis, probability and measure theory, the geometry of toric varieties, piecewise linear geometry, and error-correcting codes. Several prominent logicians, mathematicians, and computer scientists
Algebra, Meaning, and Computation ; Essays dedicated to Joseph A. Goguen on the Occasion of His 65th Birthday
This Festschrift volume - published to honor Joseph Goguen on his 65th Birthday on June 28, 2006 - includes 32 refereed papers by leading researchers in the different areas spanned by Joseph Goguen's work. The papers address a broad variety of topics from meaning, meta-logic, specification and composition, behavior and formal languages, as well as models, deduction, and computation.The papers were presented at a Symposium in San Diego, California, USA in June 2006.
Algebra and Coalgebra in Computer Science; First International Conference, CALCO 2005, Swansea, UK, September 3-6, 2005, Proceedings
CALCO, the Conference on Algebra and Coal-gebra in Computer Science, was created to bring together researchers and practitio-ners to exchange new results related to foundational aspects, and both traditional and emerging uses of algebras and coalgebras in computer science. CALCO 2005 was the first instance of this new conference. The interest that it generated in the scientific community suggests that it will not be the last. Indeed, it attracted as many as 62 submissions covering a wide range of topics roughly divided into two areas: Algebras and Coalgebras as Mathematical Objects: Automata and languages; categorical semantics; hybrid, probabilistic, and timed systems; inductive and coin-ductive methods; modal logics; relational systems and term rewriting.
Algebra 3 : Homological Algebra and Its Applications
Includes algebra, deals with important topics in homological algebra, including abstract theory of derived functors, sheaf co-homology, and an introduction to etale and l-adic co-homology. It contains four chapters which discuss homology theory in an abelian category together with some important and fundamental applications in geometry, topology, algebraic geometry (including basics in abstract algebraic geometry), and group theory.
