An inexact classical proximal point algorithm viewed as a descent method in the optimization case (1999)
- Authors:
- USP affiliated authors: HUMES JUNIOR, CARLOS - IME ; SILVA, PAULO JOSÉ DA SILVA E - IME
- Unidade: IME
- Assunto: PROGRAMAÇÃO NÃO LINEAR
- Language: Inglês
- Imprenta:
-
ABNT
HUMES JÚNIOR, Carlos e SILVA, Paulo Jose da Silva e. An inexact classical proximal point algorithm viewed as a descent method in the optimization case. . São Paulo: IME-USP. Disponível em: https://repositorio.usp.br/directbitstream/5333dc29-d188-46ae-8920-745161d8808e/1057318.pdf. Acesso em: 03 out. 2024. , 1999 -
APA
Humes Júnior, C., & Silva, P. J. da S. e. (1999). An inexact classical proximal point algorithm viewed as a descent method in the optimization case. São Paulo: IME-USP. Recuperado de https://repositorio.usp.br/directbitstream/5333dc29-d188-46ae-8920-745161d8808e/1057318.pdf -
NLM
Humes Júnior C, Silva PJ da S e. An inexact classical proximal point algorithm viewed as a descent method in the optimization case [Internet]. 1999 ;[citado 2024 out. 03 ] Available from: https://repositorio.usp.br/directbitstream/5333dc29-d188-46ae-8920-745161d8808e/1057318.pdf -
Vancouver
Humes Júnior C, Silva PJ da S e. An inexact classical proximal point algorithm viewed as a descent method in the optimization case [Internet]. 1999 ;[citado 2024 out. 03 ] Available from: https://repositorio.usp.br/directbitstream/5333dc29-d188-46ae-8920-745161d8808e/1057318.pdf - Uma nova classe de métodos de pontos proximais para programação matemática
- Rescaled proximal methods for linearly constrained convex problems
- Rescaling and stepsize selection in proximal methods using separable generalized distances
- Some inexact hybrid proximal augmented Lagrangian algorithms
- Strict convex regularizations, proximal points and augmented Lagrangians
- Rescaled proximal methods for linearly constrained convex problems
- Some inexact hybrid proximal augmented Lagrangian algorithms
- Métodos de ponto proximal, separadores e langrangeanos
- Inexact proximal point algorithms and descent methods in optimization
- Strict convex regularizations, proximal points and augmented Lagrangians
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