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Algorithms for Optimization (Hardcover)
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Algorithms for Optimization (Hardcover)
Series: The MIT Press
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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.
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