The focus of this book is the P-versus-NP Question and the theory of NP-completeness. It also provides adequate preliminaries regarding computational problems and computational models. The P-versus-NP Question asks wh...
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ISBN:
(纸本)9780521122542
The focus of this book is the P-versus-NP Question and the theory of NP-completeness. It also provides adequate preliminaries regarding computational problems and computational models. The P-versus-NP Question asks whether or not finding solutions is harder than checking the correctness of solutions. An alternative formulation asks whether or not discovering proofs is harder than verifying their correctness. It is widely believed that the answer to these equivalent formulations is positive, and this is captured by saying that P is different from NP. Although the P-versus-NP Question remains unresolved, the theory of NP-completeness offers evidence for the intractability of specific problems in NP by showing that they are universal for the entire class. Amazingly enough, NP-complete problems exist, and furthermore hundreds of natural computational problems arising in many different areas of mathematics and science are NP-complete.
From the Publisher: Presents developments in computational techniques pertaining to moving boundary problems in fluid dynamics. It describes several computational techniques which can be applied to a variety of proble...
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ISBN:
(纸本)1560324589
From the Publisher: Presents developments in computational techniques pertaining to moving boundary problems in fluid dynamics. It describes several computational techniques which can be applied to a variety of problems in thermo-fluid physics, multi-phase flow, and applied mechanics which involve moving flow boundaries. The book demonstrates the application of a variety of techniques for the numerical solution of moving boundary problems within the framework of the finite-volume approach, with appropriate examples.
Exact and approximate string matching problem is a common and often repeated task in information retrieval and bioinformatics. As current free textual databases are growing almost exponentially with the time, the stri...
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ISBN:
(纸本)9781606922613
Exact and approximate string matching problem is a common and often repeated task in information retrieval and bioinformatics. As current free textual databases are growing almost exponentially with the time, the string matching problem is becoming more expensive in terms computational times. The authors believe that recent advances in parallel and distributed processing techniques are currently mature enough and can provide powerful computing means convenient for overcoming this string matching problem. In this book the authors present a short survey for well known sequential exact and approximate string searching algorithms. Further, the authors propose four text searching implementations onto general purpose parallel computer like a cluster of heterogeneous workstations using MPI message passing library. The first three parallel implementations are based on the static and dynamic master-worker ***, the authors propose a hybrid parallel implementation that combines the advantages of static and dynamic parallel methods in order to reduce the load imbalance and communication overhead. Moreover, the authors present linear processor array architectures for flexible exact and approximate string matching. These architectures are based on parallel realisation of dynamic programming and non-deterministic finite automaton algorithms. The algorithms consist of two phases, i.e. pre-processing and searching. Then, starting from the data dependence graphs of the searching phase, parallel algorithms are derived, which can be realised directly onto special purpose processor array architectures for approximate string ***, the pre-processing phase is also accommodated onto the same processor array designs. In addition, the proposed architectures support flexible patterns i.e. patterns with a 'don't care' symbol, patterns with a complement symbol and patterns with a class symbol. Finally, this book proposes a generic design of a programmable array processor
The article briefly discusses Photogrowth, a software system designed to create images based on an algorithm inspired by the behavior of a virtual ant colony.
The article briefly discusses Photogrowth, a software system designed to create images based on an algorithm inspired by the behavior of a virtual ant colony.
Many hormones in the body oscillate with different frequencies and amplitudes, creating a dynamic environment that is essential to maintain health. In humans, disruptions to these rhythms are strongly associated with ...
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When both the variance and the N are unequal in a two-group design, the probability of a Type I error shifts from the nominal 5% error rate. The probability is too liberal when the small cell has the larger variance a...
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When both the variance and the N are unequal in a two-group design, the probability of a Type I error shifts from the nominal 5% error rate. The probability is too liberal when the small cell has the larger variance and too conservative when the large cell has the larger variance. We present an algorithm to circumvent the problem when the smaller group has the larger variance and show, by simulation, that the algorithm brings the error rate back to the nominal value without sacrificing the ability to detect true effects.
Computational models of lexical semantics, such as latent semantic analysis, can automatically generate semantic similarity measures between words from statistical redundancies in text. These measures are useful for e...
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Computational models of lexical semantics, such as latent semantic analysis, can automatically generate semantic similarity measures between words from statistical redundancies in text. These measures are useful for experimental stimulus selection and for evaluating a model's cognitive plausibility as a mechanism that people might use to organize meaning in memory. Although humans are exposed to enormous quantities of speech, practical constraints limit the amount of data that many current computational models can learn from. We follow up on previous work evaluating a simple metric of pointwise mutual information. Controlling for confounds in previous work, we demonstrate that this metric benefits from training on extremely large amounts of data and correlates more closely with human semantic similarity ratings than do publicly available implementations of several more complex models. We also present a simple tool for building simple and scalable models from large corpora quickly and efficiently.
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