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Modern Optimization Methods

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Title: Modern Optimization Methods
Description: 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.
Authors: Qingna LI
Resource Type: eBook.
Subjects: Applied mathematics
Categories: MATHEMATICS / Probability & Statistics / Regression Analysis
Database: eBook Collection (EBSCOhost)
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Header DbId: nlebk
DbLabel: eBook Collection (EBSCOhost)
An: 3724730
RelevancyScore: 1116
AccessLevel: 6
PubType: eBook
PubTypeId: ebook
PreciseRelevancyScore: 1116.28857421875
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  Data: Modern Optimization Methods
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  Label: Description
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  Data: 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.
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  Data: <searchLink fieldCode="AR" term="%22Qingna+LI%22">Qingna LI</searchLink>
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      – Code: 515
        Scheme: ddc
        Type: prePub
    Languages:
      – Code: eng
        Text: English
    Subjects:
      – SubjectFull: Applied mathematics
        Type: general
    Titles:
      – TitleFull: Modern Optimization Methods
        Type: main
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      – PersonEntity:
          Name:
            NameFull: Qingna LI
      – PersonEntity:
          Name:
            NameFull: Qingna LI
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          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2023
            – D: 29
              M: 07
              Type: profile
              Y: 2025
          Identifiers:
            – Type: isbn-print
              Value: 9782759831746
            – Type: isbn-electronic
              Value: 9782759831753
          Titles:
            – TitleFull: Modern Optimization Methods
              Type: main
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