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02903cam a2200373 4500 |
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PPN185102441 |
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http://www.sudoc.fr/185102441 |
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20190627111400.0 |
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|a 978-1-88652-928-1
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|a 1-88652-928-0
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|a (OCoLC)907028145
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|a ocn907028145
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|a 20150421h20152015k y0frey0103 ba
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|a eng
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|a US
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|a a a 001yy
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|a r
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1 |
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|a Convex optimization algorithms
|b Texte imprimé
|f Dimitri P. Bertsekas
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|a Nashua, NH
|c Athena Scientific
|d [2015]
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|a 1 vol. (XII-564 p.)
|c illustrations
|d 25 cm
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|a Comprend des références bibliographiques (pages 519-556) et un index
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|b 1. Convex optimization models : an overview
|c 1.1 Lagrange duality
|c 1.2 Fenchel duality and conic programming
|c 1.3 Additive cost problems
|c 1.4 Large number of constraints
|c 1.5 Exact penalty functions
|c 1.6 Notes, sources, and exercises
|b 2. Optimization algorithms : an overview
|c 2.1 Iterative descent algorithms
|c 2.2 Approximation methods
|c 2.3 Notes, sources, and exercises
|b 3. Subgradient methods
|c 3.1 Subgradients of convex real-valued functions
|c 3.2 Convergence analysis of subgradient methods
|c 3.3 E-subgradient methods
|c 3.4 Notes, sources, and exercises
|b 4. Polyhedral approximation methods
|c 4.1 Outer linearization - cutting plane methods
|c 4.2 Inner linearization - simplicial decomposition
|c 4.3 Duality of outer and inner linearization
|c 4.4 Generalized polyhedral approximation
|c 4.5 Generalized simplicial decomposition
|c 4.6 Polyhedral approximation for conic programming
|c 4.7 Notes, sources, and exercises
|b 5. Proximal algorithms
|c 5.1 Basic theory of proximal algorithms
|c 5.2 Dual proximal algorithms
|c 5.3 Proximal algorithms with linearization
|c 5.4 Alternating direction methods of multipliers
|c 5.5 Notes, sources, and exercises
|b 6. Additional algorithmic topics
|c 6.1 Gradient projection methods
|c 6.2 Gradient projection with extrapolation
|c 6.3 Proximal gradient methods
|c 6.4 Incremental subgradient proximal methods
|c 6.5 Coordinate descent methods
|c 6.6 Generalized proximal methods
|c 6.7 E-descent and extended monotropic programming
|c 6.8 Interior point methods
|c 6.9 Notes, sources, and exercises
|b Appendix A. Mathematical background
|b Appendix B. Convex optimization theory : a summary
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