Global convergence of model function based Bregman proximal minimization algorithms
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DOI: 10.1007/s10898-021-01114-y
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- Haihao Lu & Robert M. Freund & Yurii Nesterov, 2018. "Relatively smooth convex optimization by first-order methods, and applications," LIDAM Reprints CORE 2965, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Peter Ochs & Jalal Fadili & Thomas Brox, 2019. "Non-smooth Non-convex Bregman Minimization: Unification and New Algorithms," Journal of Optimization Theory and Applications, Springer, vol. 181(1), pages 244-278, April.
- Yurii Nesterov, 2018. "Smooth Convex Optimization," Springer Optimization and Its Applications, in: Lectures on Convex Optimization, edition 2, chapter 0, pages 59-137, Springer.
- NESTEROV, Yurii, 2007. "Gauss-Newton scheme with worst case guarantees for global performance," LIDAM Reprints CORE 1952, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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Keywords
Composite minimization; Bregman proximal minimization algorithms; Model function framework; Bregman distance; Global convergence; Kurdyka–Łojasiewicz property;All these keywords.
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