Engineering 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...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, United Kingdom
Cambridge University Press
[2022]
|
| Schlagworte: | |
| Zusammenfassung: | "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"-- |
| Beschreibung: | xiii, 637 Seiten Diagramme 26 cm |
| ISBN: | 9781108833417 |
Internformat
MARC
| LEADER | 00000nam a2200000 c 4500 | ||
|---|---|---|---|
| 001 | BV048664940 | ||
| 003 | DE-604 | ||
| 005 | 20240801 | ||
| 007 | t | ||
| 008 | 230119s2022 |||| |||| 00||| eng d | ||
| 015 | |a GBC1D5563 |2 dnb | ||
| 020 | |a 9781108833417 |q hardback |9 978-1-108-83341-7 | ||
| 035 | |a (OCoLC)1267383195 | ||
| 035 | |a (DE-599)BVBBV048664940 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-1050 | ||
| 100 | 1 | |a Martins, Joaquim R. R. A. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Engineering design optimization |c Joaquim R. R. A. Martins, Andrew Ning |
| 264 | 1 | |a Cambridge, United Kingdom |b Cambridge University Press |c [2022] | |
| 300 | |a xiii, 637 Seiten |b Diagramme |c 26 cm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 505 | 8 | |a 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 | |
| 520 | |a "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"-- | ||
| 650 | 4 | |a Engineering design / Mathematical models | |
| 650 | 4 | |a Mathematical optimization | |
| 650 | 4 | |a Multidisciplinary design optimization | |
| 650 | 4 | |a Conception technique / Modèles mathématiques | |
| 650 | 4 | |a Optimisation mathématique | |
| 650 | 4 | |a Optimisation multidisciplinaire (Conception technique) | |
| 650 | 7 | |a MATHEMATICS / Optimization |2 bisacsh | |
| 650 | 7 | |a Engineering design / Mathematical models |2 fast | |
| 650 | 7 | |a Mathematical optimization |2 fast | |
| 650 | 7 | |a Multidisciplinary design optimization |2 fast | |
| 700 | 1 | |a Ning, Andrew |e Verfasser |0 (DE-588)1252382421 |4 aut | |
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-034039565 | |
Datensatz im Suchindex
| _version_ | 1806233019326922752 |
|---|---|
| adam_text | |
| adam_txt | |
| any_adam_object | |
| any_adam_object_boolean | |
| author | Martins, Joaquim R. R. A. Ning, Andrew |
| author_GND | (DE-588)1252382421 |
| author_facet | Martins, Joaquim R. R. A. Ning, Andrew |
| author_role | aut aut |
| author_sort | Martins, Joaquim R. R. A. |
| author_variant | j r r a m jrra jrram a n an |
| building | Verbundindex |
| bvnumber | BV048664940 |
| classification_rvk | SK 870 |
| 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 |
| ctrlnum | (OCoLC)1267383195 (DE-599)BVBBV048664940 |
| discipline | Mathematik |
| discipline_str_mv | Mathematik |
| format | Book |
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| id | DE-604.BV048664940 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T21:21:54Z |
| indexdate | 2024-08-02T00:20:37Z |
| institution | BVB |
| isbn | 9781108833417 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034039565 |
| oclc_num | 1267383195 |
| open_access_boolean | |
| owner | DE-1050 |
| owner_facet | DE-1050 |
| physical | xiii, 637 Seiten Diagramme 26 cm |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | Cambridge University Press |
| record_format | marc |
| spelling | Martins, Joaquim R. R. A. Verfasser aut Engineering design optimization Joaquim R. R. A. Martins, Andrew Ning Cambridge, United Kingdom Cambridge University Press [2022] xiii, 637 Seiten Diagramme 26 cm txt rdacontent n rdamedia nc rdacarrier 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"-- Engineering design / Mathematical models Mathematical optimization Multidisciplinary design optimization Conception technique / Modèles mathématiques Optimisation mathématique Optimisation multidisciplinaire (Conception technique) MATHEMATICS / Optimization bisacsh Engineering design / Mathematical models fast Mathematical optimization fast Multidisciplinary design optimization fast Ning, Andrew Verfasser (DE-588)1252382421 aut |
| spellingShingle | Martins, Joaquim R. R. A. Ning, Andrew Engineering design optimization 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 Engineering design / Mathematical models Mathematical optimization Multidisciplinary design optimization Conception technique / Modèles mathématiques Optimisation mathématique Optimisation multidisciplinaire (Conception technique) MATHEMATICS / Optimization bisacsh Engineering design / Mathematical models fast Mathematical optimization fast Multidisciplinary design optimization fast |
| title | Engineering design optimization |
| title_auth | Engineering design optimization |
| title_exact_search | Engineering design optimization |
| title_exact_search_txtP | Engineering design optimization |
| title_full | Engineering design optimization Joaquim R. R. A. Martins, Andrew Ning |
| title_fullStr | Engineering design optimization Joaquim R. R. A. Martins, Andrew Ning |
| title_full_unstemmed | Engineering design optimization Joaquim R. R. A. Martins, Andrew Ning |
| title_short | Engineering design optimization |
| title_sort | engineering design optimization |
| topic | Engineering design / Mathematical models Mathematical optimization Multidisciplinary design optimization Conception technique / Modèles mathématiques Optimisation mathématique Optimisation multidisciplinaire (Conception technique) MATHEMATICS / Optimization bisacsh Engineering design / Mathematical models fast Mathematical optimization fast Multidisciplinary design optimization fast |
| topic_facet | Engineering design / Mathematical models Mathematical optimization Multidisciplinary design optimization Conception technique / Modèles mathématiques Optimisation mathématique Optimisation multidisciplinaire (Conception technique) MATHEMATICS / Optimization |
| work_keys_str_mv | AT martinsjoaquimrra engineeringdesignoptimization AT ningandrew engineeringdesignoptimization |