Lectures on Nonsmooth Optimization
This book provides an in-depth exploration of nonsmooth optimization, covering foundational algorithms, theoretical insights, and a wide range of applications. Nonsmooth optimization, characterized by nondifferentiable objective functions or constraints, plays a crucial role across various fields, i...
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| Format: | Elektronski eKnjiga |
| Jezik: | angleščina |
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Cham :
Springer Nature Switzerland : Imprint: Springer,
2025.
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| Izdaja: | 1st ed. 2025. |
| Serija: | Texts in Applied Mathematics,
82 |
| Teme: | |
| Online dostop: | https://doi.org/10.1007/978-3-031-91417-1 |
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Kazalo:
- Preface
- Introduction
- Convex sets and convex functions
- Subgradient and mirror descent methods
- Proximal algorithms
- Karush-Kuhn-Tucker theory and Lagrangian duality
- ADMM: alternating direction method of multipliers
- Primal dual splitting algorithms
- Error bound conditions and linear convergence
- Optimization with Kurdyka- Lojasiewicz property
- Semismooth Newton methods
- Stochastic algorithms
- References
- Index.