The.Hottest

Tuesday, October 08, 2019 , , ,
Rudra Pratap ... 288 pages - Publisher: Oxford Univ. Press; (November, 2009) ... Language: English - ISBN-10: 0199731241 - ISBN-13: 978-0199731244.

MATLAB, a software package for high-performance numerical computation and visualization, is one of the most widely used tools in the engineering field today. Its broad appeal lies in its interactive environment, which features hundreds of built-in functions for technical computation, graphics, and animation. In addition, MATLAB provides easy extensibility with its own high-level programming language. Enhanced by fun and appealing illustrations, Getting Started with MATLAB employs a casual, accessible writing style that shows users how to enjoy using MATLAB.

Features: * Discusses new features and applications, including the new engine of symbolic computation in MATLAB 7.8 (released March 2009) * Provides two sets of self guided tutorials for learning essential features of MATLAB * Includes updated commands, examples, figure, and graphs * Familiarizes users with MATLAB in just a few hours though self-guided lessons * Covers elementary, advanced, and special functions * Supplements any course that uses MATLAB * Works as a stand-alone tutorial and reference.
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Monday, October 07, 2019 , ,
Ronald E. Miller ... 676 pages - Publisher: Wiley-Interscience; (November, 1999) ... Language: English - ISBN-10: 0471351695 - ISBN-13: 978-0471351696.

A thorough and highly accessible resource for analysts in a broad range of social sciences: Optimization: Foundations and Applications presents a series of approaches to the challenges faced by analysts who must find the best way to accomplish particular objectives, usually with the added complication of constraints on the available choices. Award-winning educator Ronald E. Miller provides detailed coverage of both classical, calculus-based approaches and newer, computer-based iterative methods. Dr. Miller lays a solid foundation for both linear and nonlinear models and quickly moves on to discuss applications, including iterative methods for root-finding and for unconstrained maximization, approaches to the inequality constrained linear programming problem, and the complexities of inequality constrained maximization and minimization in nonlinear problems. Other important features include: More than 200 geometric interpretations of algebraic results, emphasizing the intuitive appeal of mathematics + Classic results mixed with modern numerical methods to aid users of computer programs + Extensive appendices containing mathematical details important for a thorough understanding of the topic. With special emphasis on questions most frequently asked by those encountering this material for the first time, Optimization: Foundations and Applications is an extremely useful resource for professionals in such areas as mathematics, engineering, economics and business, regional science, geography, sociology, political science, management and decision sciences, public policy analysis, and numerous other social sciences.
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Monday, October 07, 2019 , , ,
Steven J. Miller ... 327 pages - Publisher: American Mathematical Society; (December, 2017) ... Language: English - ISBN-10: 1470441144 - ISBN-13: 978-1470441142.

Optimization Theory is an active area of research with numerous applications; many of the books are designed for engineering classes, and thus have an emphasis on problems from such fields. Covering much of the same material, there is less emphasis on coding and detailed applications as the intended audience is more mathematical. There are still several important problems discussed (especially scheduling problems), but there is more emphasis on theory and less on the nuts and bolts of coding. A constant theme of the text is the ``why'' and the ``how'' in the subject. Why are we able to do a calculation efficiently? How should we look at a problem? Extensive effort is made to motivate the mathematics and isolate how one can apply ideas/perspectives to a variety of problems. As many of the key algorithms in the subject require too much time or detail to analyze in a first course (such as the run-time of the Simplex Algorithm), there are numerous comparisons to simpler algorithms which students have either seen or can quickly learn (such as the Euclidean algorithm) to motivate the type of results on run-time savings.
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