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Engineering design optimization / Joaquim R. R. A. Martins, Andrew Ning.

By: Contributor(s): Material type: TextPublication details: United Kingdom: Cambridge University Press, 2022Description: 1 online resourceISBN:
  • 9781108833417
Subject(s): Additional physical formats: Print version:: Engineering design optimizationDDC classification:
  • 620.0042 23 M375e
Contents:
A short history of optimization -- Numerical models and solvers -- Unconstrained gradient-based optimization -- Constrained gradient-based optimization -- Computing derivatives -- Gradient-free optimization -- Discrete optimization -- Multiobjective optimization -- Surrogate-based optimization -- Convex optimization -- Optimization under uncertainty -- Multidisciplinary design optimization.
Summary: "Based on course-tested material, this rigorous yet accessible graduate textbook covers both fundamental and advanced optimization theory and algorithms. It covers a wide range of numerical methods and topics, including both gradient-based and gradient-free algorithms, multidisciplinary design optimization, and uncertainty, with instruction on how to determine which algorithm should be used for a given application. It also provides an overview of models and how to prepare them for use with numerical optimization, including derivative computation. Over 200 high-quality visualizations and numerous examples facilitate understanding of the theory, and practical tips address common issues encountered in practical engineering design optimization and how to address them. Numerous end-of-chapter homework problems, progressing in difficulty, help put knowledge into practice"--
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Holdings
Item type Current library Call number Vol info Copy number Status Barcode
Books Library, Independent University, Bangladesh (IUB) Reference Stacks 620.0042 M375e (Browse shelf(Opens below)) 2022 01 Not For Loan 029566
Total holds: 0

Includes bibliographical references and index.

A short history of optimization -- Numerical models and solvers -- Unconstrained gradient-based optimization -- Constrained gradient-based optimization -- Computing derivatives -- Gradient-free optimization -- Discrete optimization -- Multiobjective optimization -- Surrogate-based optimization -- Convex optimization -- Optimization under uncertainty -- Multidisciplinary design optimization.

"Based on course-tested material, this rigorous yet accessible graduate textbook covers both fundamental and advanced optimization theory and algorithms. It covers a wide range of numerical methods and topics, including both gradient-based and gradient-free algorithms, multidisciplinary design optimization, and uncertainty, with instruction on how to determine which algorithm should be used for a given application. It also provides an overview of models and how to prepare them for use with numerical optimization, including derivative computation. Over 200 high-quality visualizations and numerous examples facilitate understanding of the theory, and practical tips address common issues encountered in practical engineering design optimization and how to address them. Numerous end-of-chapter homework problems, progressing in difficulty, help put knowledge into practice"--

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