Download or read book Graph Theory Approximation Methods written by N.B. Singh and published by N.B. Singh. This book was released on with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore the fascinating realm of graph theory through the lens of approximation methods in this comprehensive guide, Graph Theory: Approximation Methods . From fundamental concepts to advanced algorithms, this book delves into strategies for solving complex optimization problems in networks, offering insights and techniques essential for both students and researchers in the field. Discover practical applications, theoretical foundations, and cutting-edge developments that shape the future of graph theory and its computational applications.
Download or read book Faster Algorithms Via Approximation Theory written by Sushant Sachdeva and published by . This book was released on 2014-03-28 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: Faster Algorithms via Approximation Theory illustrates how classical and modern techniques from approximation theory play a crucial role in obtaining results that are relevant to the emerging theory of fast algorithms. The key lies in the fact that such results imply faster ways to approximate primitives such as products of matrix functions with vectors and, to compute matrix eigenvalues and eigenvectors, which are fundamental to many spectral algorithms. The first half of the book is devoted to the ideas and results from approximation theory that are central, elegant, and may have wider applicability in theoretical computer science. These include not only techniques relating to polynomial approximations but also those relating to approximations by rational functions and beyond. The remaining half illustrates a variety of ways that these results can be used to design fast algorithms. Faster Algorithms via Approximation Theory is self-contained and should be of interest to researchers and students in theoretical computer science, numerical linear algebra, and related areas.
Download or read book Graph Theory Combinatorics and Algorithms written by Martin Charles Golumbic and published by Springer Science & Business Media. This book was released on 2006-03-30 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph Theory, Combinatorics and Algorithms: Interdisciplinary Applications focuses on discrete mathematics and combinatorial algorithms interacting with real world problems in computer science, operations research, applied mathematics and engineering. The book contains eleven chapters written by experts in their respective fields, and covers a wide spectrum of high-interest problems across these discipline domains. Among the contributing authors are Richard Karp of UC Berkeley and Robert Tarjan of Princeton; both are at the pinnacle of research scholarship in Graph Theory and Combinatorics. The chapters from the contributing authors focus on "real world" applications, all of which will be of considerable interest across the areas of Operations Research, Computer Science, Applied Mathematics, and Engineering. These problems include Internet congestion control, high-speed communication networks, multi-object auctions, resource allocation, software testing, data structures, etc. In sum, this is a book focused on major, contemporary problems, written by the top research scholars in the field, using cutting-edge mathematical and computational techniques.
Download or read book An Algorithmic Theory of Numbers Graphs and Convexity written by Laszlo Lovasz and published by SIAM. This book was released on 1987-01-01 with total page 95 pages. Available in PDF, EPUB and Kindle. Book excerpt: Studies two algorithms in detail: the ellipsoid method and the simultaneous diophantine approximation method.
Download or read book Fast Approximation Algorithms for Graph Partitioning Using Spectral and Semidefinite Programming Techniques written by Lorenzo Orecchia and published by . This book was released on 2011 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph partitioning problems are a central topic of research in the study of approximation algorithms. They are of interest to theoretical computer scientists for their far-reaching connections to spectral graph theory, metric embeddings and inapproximability theory. And they are also important for many practitioners, as algorithms for graph partitioning are often fundamental primitives in the solution of other problems, such as image segmentation, clustering and social-network analysis. While many theoretical approximation algorithms exist for graph partitioning, they often rely on multicommodity-flow computations that run quadratic time in the worst case and are too time-consuming for the massive graphs that are prevalent in today's applications. In this thesis, we study the design of approximation algorithms that yield strong approximation ratios, while running in subquadratic time and relying on computational procedures that are often fast in practice. Our algorithms employ spectral and s-t flow operations to explore the cuts of a graph in a very efficient way. A crucial ingredient in their design is the usage of random walks that capture the sparse cuts of a graph better than single eigenvectors. The analysis of our methods is particularly simple, as it relies on a semidefinite programming formulation of the graph partitioning problem of choice. Indeed, we can develop our algorithms as primal-dual methods for solving a semidefinite program and show that certain random walks arise naturally from this approach.
Download or read book Approximation Methods for Efficient Learning of Bayesian Networks written by Carsten Riggelsen and published by IOS Press. This book was released on 2008 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: This publication offers and investigates efficient Monte Carlo simulation methods in order to realize a Bayesian approach to approximate learning of Bayesian networks from both complete and incomplete data. For large amounts of incomplete data when Monte Carlo methods are inefficient, approximations are implemented, such that learning remains feasible, albeit non-Bayesian. The topics discussed are: basic concepts about probabilities, graph theory and conditional independence; Bayesian network learning from data; Monte Carlo simulation techniques; and, the concept of incomplete data. In order t.
Download or read book Graph Theory and Combinatorial Optimization written by David Avis and published by Springer Science & Business Media. This book was released on 2005-12-06 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph theory is very much tied to the geometric properties of optimization and combinatorial optimization. Moreover, graph theory's geometric properties are at the core of many research interests in operations research and applied mathematics. Its techniques have been used in solving many classical problems including maximum flow problems, independent set problems, and the traveling salesman problem. Graph Theory and Combinatorial Optimization explores the field's classical foundations and its developing theories, ideas and applications to new problems. The book examines the geometric properties of graph theory and its widening uses in combinatorial optimization theory and application. The field's leading researchers have contributed chapters in their areas of expertise.
Download or read book Algorithms Graphs and Computers written by Bellman and published by Academic Press. This book was released on 1970-04-01 with total page 267 pages. Available in PDF, EPUB and Kindle. Book excerpt: Algorithms, Graphs, and Computers
Download or read book Approximation Algorithms and Semidefinite Programming written by Bernd Gärtner and published by Springer Science & Business Media. This book was released on 2012-01-10 with total page 253 pages. Available in PDF, EPUB and Kindle. Book excerpt: Semidefinite programs constitute one of the largest classes of optimization problems that can be solved with reasonable efficiency - both in theory and practice. They play a key role in a variety of research areas, such as combinatorial optimization, approximation algorithms, computational complexity, graph theory, geometry, real algebraic geometry and quantum computing. This book is an introduction to selected aspects of semidefinite programming and its use in approximation algorithms. It covers the basics but also a significant amount of recent and more advanced material. There are many computational problems, such as MAXCUT, for which one cannot reasonably expect to obtain an exact solution efficiently, and in such case, one has to settle for approximate solutions. For MAXCUT and its relatives, exciting recent results suggest that semidefinite programming is probably the ultimate tool. Indeed, assuming the Unique Games Conjecture, a plausible but as yet unproven hypothesis, it was shown that for these problems, known algorithms based on semidefinite programming deliver the best possible approximation ratios among all polynomial-time algorithms. This book follows the “semidefinite side” of these developments, presenting some of the main ideas behind approximation algorithms based on semidefinite programming. It develops the basic theory of semidefinite programming, presents one of the known efficient algorithms in detail, and describes the principles of some others. It also includes applications, focusing on approximation algorithms.
Download or read book Approximation Randomization and Combinatorial Optimization Algorithms and Techniques written by Sanjeev Arora and published by Springer. This book was released on 2003-12-15 with total page 418 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the joint refereed proceedings of the 6th International Workshop on Approximation Algorithms for Optimization Problems, APPROX 2003 and of the 7th International Workshop on Randomization and Approximation Techniques in Computer Science, RANDOM 2003, held in Princeton, NY, USA in August 2003. The 33 revised full papers presented were carefully reviewed and selected from 74 submissions. Among the issues addressed are design and analysis of randomized and approximation algorithms, online algorithms, complexity theory, combinatorial structures, error-correcting codes, pseudorandomness, derandomization, network algorithms, random walks, Markov chains, probabilistic proof systems, computational learning, randomness in cryptography, and various applications.
Download or read book An Introduction to the Approximation of Functions written by Theodore J. Rivlin and published by Courier Corporation. This book was released on 1981-01-01 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mathematics of Computing -- Numerical Analysis.
Download or read book Graph Theory Binary Optimization written by N.B. Singh and published by N.B. Singh. This book was released on with total page 159 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Graph Theory: Binary Optimization" introduces fundamental graph theory concepts and their practical applications in binary optimization. This beginner-friendly book explains how graphs model real-world problems like network design and scheduling, equipping readers with essential skills in optimization techniques.
Download or read book Approximation Algorithms for NP hard Problems written by Dorit S. Hochbaum and published by Course Technology. This book was released on 1997 with total page 632 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first book to fully address the study of approximation algorithms as a tool for coping with intractable problems. With chapters contributed by leading researchers in the field, this book introduces unifying techniques in the analysis of approximation algorithms. APPROXIMATION ALGORITHMS FOR NP-HARD PROBLEMS is intended for computer scientists and operations researchers interested in specific algorithm implementations, as well as design tools for algorithms. Among the techniques discussed: the use of linear programming, primal-dual techniques in worst-case analysis, semidefinite programming, computational geometry techniques, randomized algorithms, average-case analysis, probabilistically checkable proofs and inapproximability, and the Markov Chain Monte Carlo method. The text includes a variety of pedagogical features: definitions, exercises, open problems, glossary of problems, index, and notes on how best to use the book.
Download or read book Approximation Randomization and Combinatorial Optimization Algorithms and Techniques written by Chandra Chekuri and published by Springer Science & Business Media. This book was released on 2005-08-08 with total page 504 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the joint refereed proceedings of the 8th International Workshop on Approximation Algorithms for Combinatorial Optimization Problems, APPROX 2005 and the 9th International Workshop on Randomization and Computation, RANDOM 2005, held in Berkeley, CA, USA in August 2005. The volume contains 41 carefully reviewed papers, selected by the two program committees from a total of 101 submissions. Among the issues addressed are design and analysis of approximation algorithms, hardness of approximation, small space and data streaming algorithms, sub-linear time algorithms, embeddings and metric space methods, mathematical programming methods, coloring and partitioning, cuts and connectivity, geometric problems, game theory and applications, network design and routing, packing and covering, scheduling, design and analysis of randomized algorithms, randomized complexity theory, pseudorandomness and derandomization, random combinatorial structures, random walks/Markov chains, expander graphs and randomness extractors, probabilistic proof systems, random projections and embeddings, error-correcting codes, average-case analysis, property testing, computational learning theory, and other applications of approximation and randomness.
Download or read book Algebraic Methods in Graph Theory written by László Lovász and published by . This book was released on 1981 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Graph Theory Adiabatic Quantum Computing Methods written by N.B. Singh and published by N.B. Singh. This book was released on with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Graph Theory: Adiabatic Quantum Computing Methods" explores the convergence of quantum computing and graph theory, offering a comprehensive examination of how quantum algorithms can tackle fundamental graph problems. From foundational concepts to advanced applications in fields like cryptography, machine learning, and network analysis, this book provides a clear pathway into the evolving landscape of quantum-enhanced graph algorithms. Designed for researchers, students, and professionals alike, it bridges theoretical insights with practical implementations, paving the way for innovative solutions in computational graph theory.
Download or read book Algorithms ESA 98 written by Gianfranco Bilardi and published by Springer. This book was released on 1998-01-01 with total page 528 pages. Available in PDF, EPUB and Kindle. Book excerpt: 9
