the Academic NEST

Jaroslaw Sobieszczanski-Sobieski, Alan Morris, Michel van Tooren ... 

408 pages - Publisher: Wiley; 1st edition (September 28, 2015) ...
Language: English - ISBN-10: 1118492129 - ISBN-13: 978-1118492123 ... 

Multidisciplinary Design Optimization supported by Knowledge Based Engineering supports engineers confronting this daunting and new design paradigm. It describes methodology for conducting a system design in a systematic and rigorous manner that supports human creativity to optimize the design objective(s) subject to constraints and uncertainties.  The material presented builds on decades of experience in Multidisciplinary Design Optimization (MDO) methods, progress in concurrent computing, and Knowledge Based Engineering (KBE) tools.

Key features: Comprehensively covers MDO and is the only book to directly link this with KBE methods. * Provides a pathway through basic optimization methods to MDO methods. * Directly links design optimization methods to the massively concurrent computing technology. * Emphasizes real world engineering design practice in the application of optimization methods.

Multidisciplinary Design Optimization supported by Knowledge Based Engineering is a one-stop-shop guide to the state-of-the-art tools in the MDO and KBE disciplines for systems design engineers and managers. Graduate or post-graduate students can use it to support their design courses, and researchers or developers of computer-aided design methods will find it useful as a wide-ranging reference.

Katta G. Murty ... 502 pages - Publisher: Springer; 2010 edition (March 14, 2010)
Language: English - ASIN: B008BA5X7Q 

Linear programming (LP), modeling, and optimization are very much the fundamentals of OR, and no academic program is complete without them. No matter how highly developed one’s LP skills are, however, if a fine appreciation for modeling isn’t developed to make the best use of those skills, then the truly ‘best solutions’ are often not realized, and efforts go wasted.

Katta Murty studied LP with George Dantzig, the father of linear programming, and has written the graduate-level solution to that problem. While maintaining the rigorous LP instruction required, Murty's new book is unique in his focus on developing modeling skills to support valid decision making for complex real world problems. He describes the approach as 'intelligent modeling and decision making' to emphasize the importance of employing the best expression of actual problems and then applying the most computationally effective and efficient solution technique for that model.

From Back Cover: Optimization for Decision Making: Linear and Quadratic Models is a first-year graduate level text that illustrates how to formulate real world problems using linear and quadratic models; how to use efficient algorithms – both old and new – for solving these models; and how to draw useful conclusions and derive useful planning information from the output of these algorithms. While almost all the best known books on LP are essentially mathematics books with only very simple modeling examples, this book emphasizes the intelligent modeling of real world problems, and the author presents several illustrative examples and includes many exercises from a variety of application areas.

Additionally, where other books on LP only discuss the simplex method, and perhaps existing interior point methods, this book also discusses a new method based on using the sphere which uses matrix inversion operations sparingly and may be well suited to solving large-scale LPs, as well as those that may not have the property of being very sparse. Individual chapters present a brief history of mathematical modeling; methods for formulating real world problems; three case studies that illustrate the need for intelligent modeling; classical theory of polyhedral geometry that plays an important part in the study of LP; duality theory, optimality conditions for LP, and marginal analysis; variants of the revised simplex method; interior point methods; sphere methods; and extensions of sphere method to convex and nonconvex quadratic programs and to 0-1 integer programs through quadratic formulations. End of chapter exercises are provided throughout, with additional exercises available online.

Nature-Inspired Optimization Algorithms provides a systematic introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning and control, as well as multi-objective optimization. This book can serve as an introductory book for graduates, doctoral students and lecturers in computer science, engineering and natural sciences. It can also serve a source of inspiration for new applications. Researchers and engineers as well as experienced experts will also find it a handy reference. * * * Discusses and summarizes the latest developments in nature-inspired algorithms with comprehensive, timely literature + Provides a theoretical understanding as well as practical implementation hints + Provides a step-by-step introduction to each algorithm.

This textbook examines a broad range of problems in science and engineering, describing key numerical methods applied to real life. The case studies presented are in such areas as data fitting, vehicle route planning and optimal control, scheduling and resource allocation, sensitivity calculations and worst-case analysis.

Chapters are self-contained with exercises provided at the end of most sections. Nonlinear Optimization with Engineering Applications is ideal for self-study and classroom use in engineering courses at the senior undergraduate or graduate level. The book will also appeal to postdocs and advanced researchers interested in the development and use of optimization algorithms.

Among the main topics covered: One-variable optimization ― optimality conditions, direct search and gradient * unconstrained optimization in n variables ― solution methods including Nelder and Mead simplex, steepest descent, Newton, Gauss–Newton, and quasi-Newton techniques, trust regions and conjugate gradients * constrained optimization in n variables ― solution methods including reduced-gradients, penalty and barrier methods, sequential quadratic programming, and interior point techniques* an introduction to global optimization * an introduction to automatic differentiation.

Kurt Marti ... 368 pages - Publisher: Springer; 3rd edition (February, 2015) ... Language: English - ISBN-10: 3662462133 - ISBN-13: 978-3662462133 ...

This book examines optimization problems that in practice involve random model parameters. It details the computation of robust optimal solutions, i.e., optimal solutions that are insensitive with respect to random parameter variations, where appropriate deterministic substitute problems are needed. Based on the probability distribution of the random data and using decision theoretical concepts, optimization problems under stochastic uncertainty are converted into appropriate deterministic substitute problems.Due to the probabilities and expectations involved, the book also shows how to apply approximative solution techniques. Several deterministic and stochastic approximation methods are provided: Taylor expansion methods, regression and response surface methods (RSM), probability inequalities, multiple linearization of survival/failure domains, discretization methods, convex approximation/deterministic descent directions/efficient points, stochastic approximation and gradient procedures and differentiation formulas for probabilities and expectations. In the third edition, this book further develops stochastic optimization methods. In particular, it now shows how to apply stochastic optimization methods to the approximate solution of important concrete problems arising in engineering, economics and operations research.