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Published **1990**
by North-Holland, Distributors for the U.S. and Canada, Elsevier Science Pub. Co. in Amsterdam, New York, New York, N.Y., U.S.A .

Written in English

- Approximation theory -- Data processing.,
- Mathematical optimization -- Data processing.

**Edition Notes**

Includes bibliographical references and index.

Statement | International Association for Mathematics and Computers in Simulation ; edited by A.G. Law and C.L. Wang. |

Contributions | Law, Alan G. 1936-, Wang, C. L., International Association for Mathematics and Computers in Simulation. |

Classifications | |
---|---|

LC Classifications | QA221 .A64 1990 |

The Physical Object | |

Pagination | xv, 442 p. : |

Number of Pages | 442 |

ID Numbers | |

Open Library | OL1855083M |

ISBN 10 | 0444886931 |

LC Control Number | 90006893 |

Get this from a library! Approximation, optimization, and computing: theory and applications. [Alan G Law; C L Wang; International Association for Mathematics and Computers in Simulation.;]. Computing the Posterior Mean. In Bayesian computations we often want to compute the posterior mean of a parameter given the observed data. If \(y\) represents data we observe and \(y\) comes from the distribution \(f(y\mid\theta)\) with parameter \(\theta\) and \(\theta\) has a prior distribution \(\pi(\theta)\), then we usually want to compute the posterior distribution \(p(\theta\mid y. In computer science and operations research, approximation algorithms are efficient algorithms that find approximate solutions to optimization problems (in particular NP-hard problems) with provable guarantees on the distance of the returned solution to the optimal one. Approximation algorithms naturally arise in the field of theoretical computer science as a consequence of the widely believed. However, the book might not be suitable for self-study by younger graduate students due to the advanced mathematical style. For the more advanced reader the book seems to be an excellent in-depth resource on approximation algorithms, from their beginning up to the latest developments. Online Computing Reviews Service.

This book covers the dominant theoretical approaches to the approximate solution of hard combinatorial optimization and enumeration problems. It contains elegant combinatorial theory, useful and interesting algorithms, and deep results about the intrinsic complexity of combinatorial problems. Approximate computing is a computation technique which returns a possibly inaccurate result rather than a guaranteed accurate result, and can be used for applications where an approximate result is sufficient for its purpose. One example of such situation is for a search engine where no exact answer may exist for a certain search query and hence, many answers may be acceptable. This book shows how to design approximation algorithms: efficient algorithms that find provably near-optimal solutions. The book is organized around central algorithmic techniques for designing approximation algorithms, including greedy and local search algorithms, dynamic programming, linear and semidefinite programming, and randomization. Design and Analysis of Approximation Algorithms Pdf In addition, it can be utilized as a reference book for researchers in the region of design and analysis of approximation calculations. Design and Analysis of Approximation Algorithms is a grad course in theoretical computer science taught broadly in the universities, both in the USA and overseas.

Computing Methods in Optimization Problems deals with hybrid computing methods and optimization techniques using computers. One paper discusses different numerical approaches to optimizing trajectories, including the gradient method, the second variation method, and a generalized Newton-Raphson method. Estimation of the Necessary Sample Size for Approximation of Stochastic Optimization Problems with Probabilistic Criteria. Mathematical Optimization Theory and Operations Research, () Data-driven decision making in power systems with probabilistic guarantees: Theory and applications of chance-constrained optimization. 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. Journal of Computing and Information Science in Engineering Journal of Dynamic Systems, Measurement, and Control Journal of Electrochemical Energy Conversion and Storage.

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