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Computational Complexity of SAT XSAT and NAE-SAT ab 79.9 € als Taschenbuch: For linear and mixed Horn CNF formulas. Aus dem Bereich: Bücher, English, International, Gebundene Ausgaben,

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Volume 1: Preface. Mixed-Integer Nonlinear Optimization in Process Synthesis; C.S. Adjiman, et al. Approximate Algorithms and Heuristics for MAX-SAT; R. Battiti, M. Protasi. Connections between Nonlinear Programming and Discrete Optimization; F. Giannessi, F. Tardella. Interior Point Methods for Combinatorial Optimization; J.E. Mitchell, et al. Knapsack Problems; D. Pisinger, P. Toth. Fractional Combinatorial Optimization; T. Radzik. Reformulation-Linearization Techniques for Discrete Optimization Problems; H.D. Sherali, W.P. Adams. Gröbner Bases in Integer Programming; R.R. Thomas. Applications of Set Covering, Set Packing and Set Partitioning Models: A Survey; R.R. Vemuganti. Volume 2: Preface. Efficient Algorithms for Geometric Shortest Path Query Problems; D.Z. Chen. Computing Distances between Evolutionary Trees; B. DasGupta, et al. Combinatorial Optimization and Coalition Games; Xiaotie Deng. Steiner Minimal Trees: An Introduction, Parallel Computation, and Future Work; F.C. Harris, Jr. Resource Allocation Problems; N. Katoh, T. Ibaraki. Combinatorial Optimization in Clustering; B. Mirkin, I. Muchnik. The Graph Coloring Problem: A Bibliographic Survey; P.M. Pardalos, et al. Steiner Minimal Trees in E3: Theory, Algorithms, and Applications; J. MacGregor Smith. Dynamical System Approaches to Combinatorial Optimization; J. Starke, M. Schanz. On-Line Dominating Set Problems for Graphs; Wen-Guey Tzeng. Optimization Problems in Optical Networks; Peng-Jun Wan. Shortest Networks on Surfaces; Jia Feng Weng. Minimum Weight Triangulation; Yanfeng Xu. Optimization Applications in the Airline Industry; Gang Yu, Jian Yang.Author Index. Subject Index. Volume 3: Preface. Semidefinite Relaxations, Multivariate Normal Distributions, and Order Statistics; D. Bertsimas, Yingyu Ye. A Review of Machine Scheduling: Complexity, Algorithms and Approximability; Bo Chen, et al. Routing and Topology Embedding in Lightwave Networks; Feng Cao. The Quadratic Assignment Problem; R.E. Burkard, et al. Algorithmic Aspects of Domination in Graphs; G.J. Chang. Selected Algorithmic Techniques for Parallel Optimization; R.C. Corrêa, et al. Multispace Search in Combinatorial Optimization; Jun Gu. The Equitable Coloring of Graphs; Ko-Wei Lih. Randomized Parallel Algorithms for Combinatorial Optimization; S. Rajasekaran, J.D.P. Rolim. Tabu Search; F. Glover, M. Laguna. Author Index. Subject Index. Author Index of Volumes 1&endash;3. Subject Index of Volumes 1&endash;3.

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Jane Austen is a mystery. The first incontrovertibly great woman novelist, she is, among other things, one of the finest prose stylists in literature; the first truly modern writer, the godmother of chick lit. She is also the greatest enigma (next to Shakespeare) in English literature. Soldiers in the First World War sat in the trenches and read her novels for the civilising comforts they provided. Hard-nut literary critics such as F. R. Leavis lauded their austere complexity. World Book Day, 2007, found that Pride and Prejudice was the one book "the nation can't live without". In this witty, accessible guide, Charles Jennings goes in search of this enigma through her words as well as her times, including a short biography, an overview of the novels, as well as the world that she inhabited. Finally, the book contains Jane's very own words of advice for the modern life. Charles Jennings studied English at Oxford and then become, as reported in the Spectator, the funniest journalist in London. He has written numerous books, including a history of Greenwich, as well as Them and Us: The American Invasion of English High Society. 1. Language: English. Narrator: Nigel Carrington. Audio sample: http://samples.audible.de/bk/adbl/010297/bk_adbl_010297_sample.mp3. Digital audiobook in aax.

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A WALL STREET JOURNAL BESTSELLER! "You can't really know anything if you just remember isolated facts. If the facts don't hang together on a latticework of theory, you don't have them in a usable form. You've got to have models in your head." - Charlie Munger, investor, vice chairman of Berkshire Hathaway The world's greatest problem-solvers, forecasters, and decision-makers all rely on a set of frameworks and shortcuts that help them cut through complexity and separate good ideas from bad ones. They're called mental models, and you can find them in dense textbooks on psychology, physics, economics, and more. Or, you can just read Super Thinking, a fun, illustrated guide to every mental model you could possibly need. How can mental models help you? Well, here are just a few examples... • If you've ever been overwhelmed by a to-do list that's grown too long, maybe you need the Eisenhower Decision Matrix to help you prioritize. • Use the 5 Whys model to better understand people's motivations or get to the root cause of a problem. • Before concluding that your colleague who messes up your projects is out to sabotage you, consider Hanlon's Razor for an alternative explanation. • Ever sat through a bad movie just because you paid a lot for the ticket? You might be falling prey to Sunk Cost Fallacy. • Set up Forcing Functions, like standing meeting or deadlines, to help grease the wheels for changes you want to occur. So, the next time you find yourself faced with a difficult decision or just trying to understand a complex situation, let Super Thinking upgrade your brain with mental models.

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Computational Complexity of SAT XSAT and NAE-SAT ab 79.9 EURO For linear and mixed Horn CNF formulas

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The Boolean conjunctive normal form (CNF) satisability problem, called SAT for short, gets as input a CNF formula and has to decide whether this formula admits a satisfying truth assignment. As is well known, the remarkable result by S. Cook in 1971 established SAT as the first and genuine complete problem for the complexity class NP. In this thesis we consider SAT for a subclass of CNF, the so called Mixed Horn formula class (MHF). A formula F 2 MHF consists of a 2-CNF part P and a Horn part H. We propose that MHF has a central relevance in CNF because many prominent NP-complete problems, e.g. Feedback Vertex Set, Vertex Cover, Dominating Set and Hitting Set, can easily be encoded as MHF. Furthermore, we show that SAT remains NP-complete for some interesting subclasses of MHF. We also provide algorithms for some of these subclasses solving SAT in a better running time than O(2^0.5284n) which is the best bound for MHF so far. In addition, we investigate the computational complexity of some prominent variants of SAT, namely not-all-equal SAT (NAE-SAT) and exact SAT (XSAT) restricted to the class of linear CNF formulas.

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High Quality Content by WIKIPEDIA articles! The Valiant Vazirani theorem is an important result in computational complexity theory. It was proven by Leslie Valiant and Vijay Vazirani in their paper titled "NP is as easy as detecting unique solutions" published in 1986. The theorem states that if there is a polynomial time algorithm for UNIQUE-SAT, then NP=RP. The theorem implies that even if the number of satisfying assignments is very small, SAT (which is an NP-complete problem) still remains a hard problem. UNIQUE-SAT is a promise problem that decides whether a given Boolean formula is unsatisfiable or has exactly one satisfying assignment. In the first case a UNIQUE-SAT algorithm would reject, and in the second it would accept the formula. If the formula has more than one satisfying assignment then the behavior of the UNIQUE-SAT algorithm does not matter.

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NP Complete (abbreviated as NPC) problems, standing at the crux of deciding whether P=NP, are among hardest problems in computer science and other related areas. Observing that NPC problems have different natures, they can be further classified. We show that the classification of NPC problems may depend on their natures, reduction methods, exact algorithms, and the boundary between P and NP. We propose a new perspective: both P problems and NPC problems have the duality feature in terms of computational complexity of asymptotic efficiency of algorithms. We then introduce near optimal solutions to some NPC problems such as Traveling Salesman Problems (TSP), Boolean Satisfiability Problems (SAT), Scheduling algorithms in Cloud data centers and Bigdata process platforms. These solutions may shine light on other NPC problems and their applications.

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