- EAN13
- 9782759831753
- Éditeur
- EDP sciences
- Date de publication
- 13/11/2023
- Collection
- Current Natural Sciences
- Langue
- anglais
- Fiches UNIMARC
- S'identifier
Livre numérique
-
Aide EAN13 : 9782759831753
- Fichier PDF, avec Marquage en filigrane
65.99
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.
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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