Algorithms For Large Scale Nonlinear Optimization PDF Download
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Author | : Gianni Pillo |
Publisher | : Springer Science & Business Media |
Total Pages | : 297 |
Release | : 2006-06-03 |
Genre | : Mathematics |
ISBN | : 0387300651 |
Download Large-Scale Nonlinear Optimization Book 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.
Author | : Richard Alan Waltz |
Publisher | : |
Total Pages | : |
Release | : 2002 |
Genre | : |
ISBN | : |
Download Algorithms for Large-scale Nonlinear Optimization Book in PDF, ePub and Kindle
We investigate two algorithmic approaches for the efficient and robust solution of large-scale, generally constrained, nonlinear optimization problems.
Author | : A.R. Conn |
Publisher | : Springer Science & Business Media |
Total Pages | : 347 |
Release | : 2013-04-17 |
Genre | : Computers |
ISBN | : 3662122111 |
Download Lancelot Book in PDF, ePub and Kindle
LANCELOT is a software package for solving large-scale nonlinear optimization problems. This book is our attempt to provide a coherent overview of the package and its use. This includes details of how one might present examples to the package, how the algorithm tries to solve these examples and various technical issues which may be useful to implementors of the software. We hope this book will be of use to both researchers and practitioners in nonlinear programming. Although the book is primarily concerned with a specific optimization package, the issues discussed have much wider implications for the design and im plementation of large-scale optimization algorithms. In particular, the book contains a proposal for a standard input format for large-scale optimization problems. This proposal is at the heart of the interface between a user's problem and the LANCE LOT optimization package. Furthermore, a large collection of over five hundred test ex amples has already been written in this format and will shortly be available to those who wish to use them. We would like to thank the many people and organizations who supported us in our enterprise. We first acknowledge the support provided by our employers, namely the the Facultes Universitaires Notre-Dame de la Paix (Namur, Belgium), Harwell Laboratory (UK), IBM Corporation (USA), Rutherford Appleton Laboratory (UK) and the University of Waterloo (Canada). We are grateful for the support we obtained from NSERC (Canada), NATO and AMOCO (UK).
Author | : Gianni Pillo |
Publisher | : Springer |
Total Pages | : 0 |
Release | : 2008-11-01 |
Genre | : Mathematics |
ISBN | : 9780387510668 |
Download Large-Scale Nonlinear Optimization Book 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.
Author | : Lorenz T. Biegler |
Publisher | : SIAM |
Total Pages | : 411 |
Release | : 2010-01-01 |
Genre | : Science |
ISBN | : 0898719380 |
Download Nonlinear Programming Book in PDF, ePub and Kindle
This book addresses modern nonlinear programming (NLP) concepts and algorithms, especially as they apply to challenging applications in chemical process engineering. The author provides a firm grounding in fundamental NLP properties and algorithms, and relates them to real-world problem classes in process optimization, thus making the material understandable and useful to chemical engineers and experts in mathematical optimization.
Author | : Andrew R. Conn |
Publisher | : Springer Verlag |
Total Pages | : 330 |
Release | : 1992 |
Genre | : Mathematics |
ISBN | : 9780387554709 |
Download LANCELOT Book in PDF, ePub and Kindle
Author | : Richard W. Cottle |
Publisher | : Springer |
Total Pages | : 644 |
Release | : 2017-06-11 |
Genre | : Business & Economics |
ISBN | : 1493970550 |
Download Linear and Nonlinear Optimization Book in PDF, ePub and Kindle
This textbook on Linear and Nonlinear Optimization is intended for graduate and advanced undergraduate students in operations research and related fields. It is both literate and mathematically strong, yet requires no prior course in optimization. As suggested by its title, the book is divided into two parts covering in their individual chapters LP Models and Applications; Linear Equations and Inequalities; The Simplex Algorithm; Simplex Algorithm Continued; Duality and the Dual Simplex Algorithm; Postoptimality Analyses; Computational Considerations; Nonlinear (NLP) Models and Applications; Unconstrained Optimization; Descent Methods; Optimality Conditions; Problems with Linear Constraints; Problems with Nonlinear Constraints; Interior-Point Methods; and an Appendix covering Mathematical Concepts. Each chapter ends with a set of exercises. The book is based on lecture notes the authors have used in numerous optimization courses the authors have taught at Stanford University. It emphasizes modeling and numerical algorithms for optimization with continuous (not integer) variables. The discussion presents the underlying theory without always focusing on formal mathematical proofs (which can be found in cited references). Another feature of this book is its inclusion of cultural and historical matters, most often appearing among the footnotes. "This book is a real gem. The authors do a masterful job of rigorously presenting all of the relevant theory clearly and concisely while managing to avoid unnecessary tedious mathematical details. This is an ideal book for teaching a one or two semester masters-level course in optimization – it broadly covers linear and nonlinear programming effectively balancing modeling, algorithmic theory, computation, implementation, illuminating historical facts, and numerous interesting examples and exercises. Due to the clarity of the exposition, this book also serves as a valuable reference for self-study." Professor Ilan Adler, IEOR Department, UC Berkeley "A carefully crafted introduction to the main elements and applications of mathematical optimization. This volume presents the essential concepts of linear and nonlinear programming in an accessible format filled with anecdotes, examples, and exercises that bring the topic to life. The authors plumb their decades of experience in optimization to provide an enriching layer of historical context. Suitable for advanced undergraduates and masters students in management science, operations research, and related fields." Michael P. Friedlander, IBM Professor of Computer Science, Professor of Mathematics, University of British Columbia
Author | : Kang Hoh Phua |
Publisher | : |
Total Pages | : 15 |
Release | : 1996 |
Genre | : Mathematical optimization |
ISBN | : |
Download Parallel algorithms for large-scale nonlinear optimization Book in PDF, ePub and Kindle
Abstract: "Multi-step, multi-directional parallel variable metric (PVM) methods for unconstrained optimization problems are presented in this paper. These algorithms generate several VM directions at each iteration, different line search and scaling strategies are then applied in parallel along each search direction. In comparison to some serial VM methods, computational results show that a reduction of 200% or more in terms of number of iterations and function/gradient evaluations respectively could be achieved by the new parallel algorithm over a wide range of 63 test problems. In particular, when the complexity, or the size of the problem increases, greater savings could be achieved by the proposed parallel algorithm. In fact, the speedup factors gained by our PVM algorithms could be as high as 28 times for some test problems."
Author | : Guanghui Liu |
Publisher | : |
Total Pages | : |
Release | : 1999 |
Genre | : |
ISBN | : |
Download Design Issues in Algorithms for Large Scale Nonlinear Programming Book in PDF, ePub and Kindle
A product of this dissertation is a software, called NITRO, that implements an interior point method using the new algorithmic features presented in this dissertation. The performance of this code is assessed by comparing it with established software for large scale optimization (SNOPT, filterSQP and LOQO).
Author | : Urmila Diwekar |
Publisher | : Springer |
Total Pages | : 168 |
Release | : 2015-03-05 |
Genre | : Business & Economics |
ISBN | : 1493922823 |
Download BONUS Algorithm for Large Scale Stochastic Nonlinear Programming Problems Book in PDF, ePub and Kindle
This book presents the details of the BONUS algorithm and its real world applications in areas like sensor placement in large scale drinking water networks, sensor placement in advanced power systems, water management in power systems, and capacity expansion of energy systems. A generalized method for stochastic nonlinear programming based on a sampling based approach for uncertainty analysis and statistical reweighting to obtain probability information is demonstrated in this book. Stochastic optimization problems are difficult to solve since they involve dealing with optimization and uncertainty loops. There are two fundamental approaches used to solve such problems. The first being the decomposition techniques and the second method identifies problem specific structures and transforms the problem into a deterministic nonlinear programming problem. These techniques have significant limitations on either the objective function type or the underlying distributions for the uncertain variables. Moreover, these methods assume that there are a small number of scenarios to be evaluated for calculation of the probabilistic objective function and constraints. This book begins to tackle these issues by describing a generalized method for stochastic nonlinear programming problems. This title is best suited for practitioners, researchers and students in engineering, operations research, and management science who desire a complete understanding of the BONUS algorithm and its applications to the real world.