Algorithms for Minimization Without Derivatives - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Algorithms for Minimization Without Derivatives write by Richard P. Brent. This book was released on 2013-06-10. Algorithms for Minimization Without Derivatives available in PDF, EPUB and Kindle. DIVOutstanding text for graduate students and research workers proposes improvements to existing algorithms, extends their related mathematical theories, and offers details on new algorithms for approximating local and global minima. /div
Large-Scale Nonlinear Optimization
Large-Scale Nonlinear Optimization - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Large-Scale Nonlinear Optimization write by Gianni Pillo. This book was released on 2006-06-03. Large-Scale Nonlinear Optimization available in PDF, EPUB and Kindle. This book reviews and discusses recent advances in the development of methods and algorithms for nonlinear optimization and its applications, focusing on the large-dimensional case, the current forefront of much research. Individual chapters, contributed by eminent authorities, provide an up-to-date overview of the field from different and complementary standpoints, including theoretical analysis, algorithmic development, implementation issues and applications.
Introduction to Derivative-Free Optimization
Introduction to Derivative-Free Optimization - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Introduction to Derivative-Free Optimization write by Andrew R. Conn. This book was released on 2009-04-16. Introduction to Derivative-Free Optimization available in PDF, EPUB and Kindle. The first contemporary comprehensive treatment of optimization without derivatives. This text explains how sampling and model techniques are used in derivative-free methods and how they are designed to solve optimization problems. It is designed to be readily accessible to both researchers and those with a modest background in computational mathematics.
Algorithms for Optimization
Algorithms for Optimization - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Algorithms for Optimization write by Mykel J. Kochenderfer. This book was released on 2019-03-12. Algorithms for Optimization available in PDF, EPUB and Kindle. A comprehensive introduction to optimization with a focus on practical algorithms for the design of engineering systems. This book offers a comprehensive introduction to optimization with a focus on practical algorithms. The book approaches optimization from an engineering perspective, where the objective is to design a system that optimizes a set of metrics subject to constraints. Readers will learn about computational approaches for a range of challenges, including searching high-dimensional spaces, handling problems where there are multiple competing objectives, and accommodating uncertainty in the metrics. Figures, examples, and exercises convey the intuition behind the mathematical approaches. The text provides concrete implementations in the Julia programming language. Topics covered include derivatives and their generalization to multiple dimensions; local descent and first- and second-order methods that inform local descent; stochastic methods, which introduce randomness into the optimization process; linear constrained optimization, when both the objective function and the constraints are linear; surrogate models, probabilistic surrogate models, and using probabilistic surrogate models to guide optimization; optimization under uncertainty; uncertainty propagation; expression optimization; and multidisciplinary design optimization. Appendixes offer an introduction to the Julia language, test functions for evaluating algorithm performance, and mathematical concepts used in the derivation and analysis of the optimization methods discussed in the text. The book can be used by advanced undergraduates and graduate students in mathematics, statistics, computer science, any engineering field, (including electrical engineering and aerospace engineering), and operations research, and as a reference for professionals.
MM Optimization Algorithms
MM Optimization Algorithms - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook MM Optimization Algorithms write by Kenneth Lange. This book was released on 2016-07-11. MM Optimization Algorithms available in PDF, EPUB and Kindle. MM Optimization Algorithms?offers an overview of the MM principle, a device for deriving optimization algorithms satisfying the ascent or descent property. These algorithms can separate the variables of a problem, avoid large matrix inversions, linearize a problem, restore symmetry, deal with equality and inequality constraints gracefully, and turn a nondifferentiable problem into a smooth problem.? The author presents the first extended treatment of MM algorithms, which are ideal for high-dimensional optimization problems in data mining, imaging, and genomics; derives numerous algorithms from a broad diversity of application areas, with a particular emphasis on statistics, biology, and data mining; and summarizes a large amount of literature that has not reached book form before.?
