Modern Optimization Methods / / Qingna LI.
With the fast development of big data and artificial intelligence, a natural question is how do we analyze data more efficiently? One of the efficient ways is to use optimization. What is optimization? Optimization exists everywhere. People optimize. As long as you have choices, you do optimization....
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Place / Publishing House: | Les Ulis : : EDP Sciences, , [2023] 2023 |
Year of Publication: | 2023 |
Language: | English |
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Physical Description: | 1 online resource (157 p.) |
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LI, Qingna, author. aut http://id.loc.gov/vocabulary/relators/aut Modern Optimization Methods / Qingna LI. Les Ulis : EDP Sciences, [2023] 2023 1 online resource (157 p.) text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Frontmatter -- Preface -- Contents -- Chapter 1. Introduction -- Chapter 2. Fundamentals of Optimization -- Chapter 3. Line Search Methods -- Chapter 4. Trust Region Methods -- Chapter 5. Conjugate Gradient Methods -- Chapter 6. Semismooth Newton's Method -- Chapter 7. Theory of Constrained Optimization -- Chapter 8. Penalty and Augmented Lagrangian Methods -- Chapter 9. Bilevel Optimization and Its Applications -- Bibliography restricted access http://purl.org/coar/access_right/c_16ec online access with authorization star With the fast development of big data and artificial intelligence, a natural question is how do we analyze data more efficiently? One of the efficient ways is to use optimization. What is optimization? Optimization exists everywhere. People optimize. As long as you have choices, you do optimization. Optimization is the key of operations research. This book introduces the basic definitions and theory about numerical optimization, including optimality conditions for unconstrained and constrained optimization, as well as algorithms for unconstrained and constrained problems. Moreover, it also includes the nonsmooth Newton's method, which plays an important role in large-scale numerical optimization. Finally, based on the author's research experiences, several latest applications about optimization are introduced, including optimization algorithms for hypergraph matching, support vector machine and bilevel optimization approach for hyperparameter selection in machine learning. With these optimization tools, one can deal with data more efficiently. Mode of access: Internet via World Wide Web. In English. Description based on online resource; title from PDF title page (publisher's Web site, viewed 09. Dez 2023) MATHEMATICS / Probability & Statistics / Regression Analysis. bisacsh https://doi.org/10.1051/978-2-7598-3175-3 https://www.degruyter.com/isbn/9782759831753 Cover https://www.degruyter.com/document/cover/isbn/9782759831753/original |
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English |
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author |
LI, Qingna, LI, Qingna, |
spellingShingle |
LI, Qingna, LI, Qingna, Modern Optimization Methods / Frontmatter -- Preface -- Contents -- Chapter 1. Introduction -- Chapter 2. Fundamentals of Optimization -- Chapter 3. Line Search Methods -- Chapter 4. Trust Region Methods -- Chapter 5. Conjugate Gradient Methods -- Chapter 6. Semismooth Newton's Method -- Chapter 7. Theory of Constrained Optimization -- Chapter 8. Penalty and Augmented Lagrangian Methods -- Chapter 9. Bilevel Optimization and Its Applications -- Bibliography |
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LI, Qingna, LI, Qingna, |
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LI, Qingna, |
title |
Modern Optimization Methods / |
title_full |
Modern Optimization Methods / Qingna LI. |
title_fullStr |
Modern Optimization Methods / Qingna LI. |
title_full_unstemmed |
Modern Optimization Methods / Qingna LI. |
title_auth |
Modern Optimization Methods / |
title_alt |
Frontmatter -- Preface -- Contents -- Chapter 1. Introduction -- Chapter 2. Fundamentals of Optimization -- Chapter 3. Line Search Methods -- Chapter 4. Trust Region Methods -- Chapter 5. Conjugate Gradient Methods -- Chapter 6. Semismooth Newton's Method -- Chapter 7. Theory of Constrained Optimization -- Chapter 8. Penalty and Augmented Lagrangian Methods -- Chapter 9. Bilevel Optimization and Its Applications -- Bibliography |
title_new |
Modern Optimization Methods / |
title_sort |
modern optimization methods / |
publisher |
EDP Sciences, |
publishDate |
2023 |
physical |
1 online resource (157 p.) |
contents |
Frontmatter -- Preface -- Contents -- Chapter 1. Introduction -- Chapter 2. Fundamentals of Optimization -- Chapter 3. Line Search Methods -- Chapter 4. Trust Region Methods -- Chapter 5. Conjugate Gradient Methods -- Chapter 6. Semismooth Newton's Method -- Chapter 7. Theory of Constrained Optimization -- Chapter 8. Penalty and Augmented Lagrangian Methods -- Chapter 9. Bilevel Optimization and Its Applications -- Bibliography |
isbn |
9782759831753 |
url |
https://doi.org/10.1051/978-2-7598-3175-3 https://www.degruyter.com/isbn/9782759831753 https://www.degruyter.com/document/cover/isbn/9782759831753/original |
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Not Illustrated |
doi_str_mv |
10.1051/978-2-7598-3175-3 |
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AT liqingna modernoptimizationmethods |
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(DE-B1597)677655 |
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Modern Optimization Methods / |
_version_ |
1789654385229299712 |
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