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Principles of Optimal Design: Modeling and Computation
Since the first edition was published, computers have become ever more powerful, design engineers are tackling more complex systems, and the term "optimization" is now routinely used to denote a design process with increased speed and quality. This second edition takes account of these developments and brings the original text thoroughly up to date. The book now discusses trust region and convex approximation algorithms. A new chapter focuses on how to construct optimal design models. Three new case studies illustrate the creation of optimization models. The final chapter on optimization practice has been expanded to include computation of derivatives, interpretation of algorithmic results, and selection of algorithms and software.
416 pages, Paperback
First published January 1, 1988
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May 15, 2015
One of the better books covering an approach to design from a methodological approach rooted in optimization theory.
In general, if we know the design space (domain), and the design problem (objective function or range mapping), then this approach works very well. This axiomatic approach builds on previous concepts (like any rigorous mathematical framework does) and traverses through the chapters in a (mostly) linear fashion, ending in reasonably robust approximated 2nd order constrained optimization methods.
The key concept to keep in mind with this approach, and all those tangentially related, is that a large amount of empirical evidence shows that the design space and objective function are not just ill-posed, they are perhaps ephemeral in the minds of a distributed number of design stakeholders far from the beginning of the actual design process. Only after considerable distillation of core design requirements can one then move to modeling and subsequent optimization.
As a result, this book in and of itself does not cover what many in fields particularly outside of engineering would consider the design process. There are a plethora of methods dealing with such "fuzzy front end," often time using primarily qualitative methods such as focus groups and sticky notes.
This is not to say this is a major drawback with this book, however, as this book is situated to pick up on the design process precisely after such design requirements convene through the aforementioned distillation process.
My understanding is the authors are pushing forward with a 3rd edition that includes more modern methods including nongradient serach methods, combined control and optimization, and decomposition-based strategies. :-)
In general, if we know the design space (domain), and the design problem (objective function or range mapping), then this approach works very well. This axiomatic approach builds on previous concepts (like any rigorous mathematical framework does) and traverses through the chapters in a (mostly) linear fashion, ending in reasonably robust approximated 2nd order constrained optimization methods.
The key concept to keep in mind with this approach, and all those tangentially related, is that a large amount of empirical evidence shows that the design space and objective function are not just ill-posed, they are perhaps ephemeral in the minds of a distributed number of design stakeholders far from the beginning of the actual design process. Only after considerable distillation of core design requirements can one then move to modeling and subsequent optimization.
As a result, this book in and of itself does not cover what many in fields particularly outside of engineering would consider the design process. There are a plethora of methods dealing with such "fuzzy front end," often time using primarily qualitative methods such as focus groups and sticky notes.
This is not to say this is a major drawback with this book, however, as this book is situated to pick up on the design process precisely after such design requirements convene through the aforementioned distillation process.
My understanding is the authors are pushing forward with a 3rd edition that includes more modern methods including nongradient serach methods, combined control and optimization, and decomposition-based strategies. :-)
Displaying 1 of 1 review