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Author: Heribert Vollmer
Publisher: Springer Science & Business Media
Published: 2013-04-17
Total Pages: 277
ISBN-13: 3662039273
DOWNLOAD EBOOK →An advanced textbook giving a broad, modern view of the computational complexity theory of boolean circuits, with extensive references, for theoretical computer scientists and mathematicians.
Author: Stasys Jukna
Publisher: Springer Science & Business Media
Published: 2012-01-06
Total Pages: 618
ISBN-13: 3642245080
DOWNLOAD EBOOK →Boolean circuit complexity is the combinatorics of computer science and involves many intriguing problems that are easy to state and explain, even for the layman. This book is a comprehensive description of basic lower bound arguments, covering many of the gems of this “complexity Waterloo” that have been discovered over the past several decades, right up to results from the last year or two. Many open problems, marked as Research Problems, are mentioned along the way. The problems are mainly of combinatorial flavor but their solutions could have great consequences in circuit complexity and computer science. The book will be of interest to graduate students and researchers in the fields of computer science and discrete mathematics.
Author: Sanjeev Arora
Publisher: Cambridge University Press
Published: 2009-04-20
Total Pages: 609
ISBN-13: 0521424267
DOWNLOAD EBOOK →New and classical results in computational complexity, including interactive proofs, PCP, derandomization, and quantum computation. Ideal for graduate students.
Author: Allan Heydon
Publisher:
Published: 1990
Total Pages: 55
ISBN-13:
DOWNLOAD EBOOK →These ideas are the b̀uilding blocks' of the proof itself. A brief history of related result is given. Then, an intuitive description of the proof and a r̀oad map' of its structure (which has several levels and branches) are presented to provide an overall gist of what is going on behind the formal mathematics which follow. The heart of the proof is the so-called S̀witching Lemma', which is given considerable attention. The main result and a corollary are then stated and proven."
Author: Daniel Pierre Bovet
Publisher: Prentice Hall PTR
Published: 1994
Total Pages: 304
ISBN-13:
DOWNLOAD EBOOK →Using a balanced approach that is partly algorithmic and partly structuralist, this book systematically reviews the most significant results obtained in the study of computational complexity theory. Features over 120 worked examples, over 200 problems, and 400 figures.
Author: Amir Shpilka
Publisher: Now Publishers Inc
Published: 2010
Total Pages: 193
ISBN-13: 1601984006
DOWNLOAD EBOOK →A large class of problems in symbolic computation can be expressed as the task of computing some polynomials; and arithmetic circuits form the most standard model for studying the complexity of such computations. This algebraic model of computation attracted a large amount of research in the last five decades, partially due to its simplicity and elegance. Being a more structured model than Boolean circuits, one could hope that the fundamental problems of theoretical computer science, such as separating P from NP, will be easier to solve for arithmetic circuits. However, in spite of the appearing simplicity and the vast amount of mathematical tools available, no major breakthrough has been seen. In fact, all the fundamental questions are still open for this model as well. Nevertheless, there has been a lot of progress in the area and beautiful results have been found, some in the last few years. As examples we mention the connection between polynomial identity testing and lower bounds of Kabanets and Impagliazzo, the lower bounds of Raz for multilinear formulas, and two new approaches for proving lower bounds: Geometric Complexity Theory and Elusive Functions. The goal of this monograph is to survey the field of arithmetic circuit complexity, focusing mainly on what we find to be the most interesting and accessible research directions. We aim to cover the main results and techniques, with an emphasis on works from the last two decades. In particular, we discuss the recent lower bounds for multilinear circuits and formulas, the advances in the question of deterministically checking polynomial identities, and the results regarding reconstruction of arithmetic circuits. We do, however, also cover part of the classical works on arithmetic circuits. In order to keep this monograph at a reasonable length, we do not give full proofs of most theorems, but rather try to convey the main ideas behind each proof and demonstrate it, where possible, by proving some special cases.
Author: Johannes A. Buchmann
Publisher: American Mathematical Society
Published: 2024-03-18
Total Pages: 391
ISBN-13: 1470473984
DOWNLOAD EBOOK →Quantum algorithms are among the most important, interesting, and promising innovations in information and communication technology. They pose a major threat to today's cybersecurity and at the same time promise great benefits by potentially solving previously intractable computational problems with reasonable effort. The theory of quantum algorithms is based on advanced concepts from computer science, mathematics, and physics. Introduction to Quantum Algorithms offers a mathematically precise exploration of these concepts, accessible to those with a basic mathematical university education, while also catering to more experienced readers. This comprehensive book is suitable for self-study or as a textbook for one- or two-semester introductory courses on quantum computing algorithms. Instructors can tailor their approach to emphasize theoretical understanding and proofs or practical applications of quantum algorithms, depending on the course's goals and timeframe.
Author: Ian Parberry
Publisher: MIT Press
Published: 1994
Total Pages: 312
ISBN-13: 9780262161480
DOWNLOAD EBOOK →Neural networks usually work adequately on small problems but can run into trouble when they are scaled up to problems involving large amounts of input data. Circuit Complexity and Neural Networks addresses the important question of how well neural networks scale - that is, how fast the computation time and number of neurons grow as the problem size increases. It surveys recent research in circuit complexity (a robust branch of theoretical computer science) and applies this work to a theoretical understanding of the problem of scalability. Most research in neural networks focuses on learning, yet it is important to understand the physical limitations of the network before the resources needed to solve a certain problem can be calculated. One of the aims of this book is to compare the complexity of neural networks and the complexity of conventional computers, looking at the computational ability and resources (neurons and time) that are a necessary part of the foundations of neural network learning. Circuit Complexity and Neural Networks contains a significant amount of background material on conventional complexity theory that will enable neural network scientists to learn about how complexity theory applies to their discipline, and allow complexity theorists to see how their discipline applies to neural networks.
