Practical Mathematical Optimization
Basic Optimization Theory and Gradient-Based Algorithms
Snyman, Jan ; Wilke, Daniel N. 2018 Springer International Publishing
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- Link zu diesem Datensatz
- https://d-nb.info/1157014283
- Titel
- Practical Mathematical Optimization : Basic Optimization Theory and Gradient-Based Algorithms
- Art des Inhalts
- Monographie
- Autor(en)
- Organisation(en)
-
- Springer International Publishing
- Auflage
- 2nd edition 2018
- Verlag
- Cham : Springer International Publishing [2018]
- Jahr
- Erscheinungsdatum: 2018
- Umfang/Format
- Online-Ressource
- ISBN/Einband/Preis
9783319775869- DOI
- 10.1007/978-3-319-77586-9
- Online
- https://doi.org/10.1007/978-3-319-77586-9
- Sprache
- eng
- Schlagwörter
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- Mathematica
- algorithms
- linear optimization
- optimization
- programming
- Python
- multi-modal optimization
- non-smooth optimization
- discontinuous optimization
- Numerical Linear Algebra
- Hessian matrix approximations
- Gradient-only solution strategies
- Karush-Kuhn-Tucker theory
- Quadratic programming
- line search descent algorithm for unconstrained minimization
- Unconstrained one-dimensional minimization
- (Springer Nature Marketing Classification)B
- (Springer Nature Subject Code)SCM26008: Optimization
- (Springer Nature Subject Collection)SUCO11649: Mathematics and Statistics
- (Springer Nature Subject Code)SCM14018: Algorithms
- (Springer Nature Subject Code)SCM26024: Operations Research, Management Science
- (Springer Nature Subject Code)SCM14050: Numerical Analysis
- (Springer Nature Subject Code)SCM14042: Mathematical Software
- (Springer Nature Subject Code)SCM12171: Real Functions
- Anmerkungen
- Lizenzpflichtig
- Stand
- 05.04.2025 08:27
- Im Katalog seit
- 07.03.2026