A Newton-like method with mixed factorizations and cubic regularization and its usage in an Augmented Lagrangian framework (2019)
- Authors:
- Autor USP: BIRGIN, ERNESTO JULIAN GOLDBERG - IME
- Unidade: IME
- Subjects: OTIMIZAÇÃO MATEMÁTICA; PROGRAMAÇÃO MATEMÁTICA
- Language: Inglês
- Imprenta:
- Publisher: Weierstrass Institute for Applied Analysis and Stochastics (WIAS)
- Publisher place: Berlin
- Date published: 2019
- Source:
- Título: Conference book
- Conference titles: International Conference on Continuous Optimization - ICCOPT
-
ABNT
BIRGIN, Ernesto Julian Goldberg e MARTÍNEZ, José Mário. A Newton-like method with mixed factorizations and cubic regularization and its usage in an Augmented Lagrangian framework. 2019, Anais.. Berlin: Weierstrass Institute for Applied Analysis and Stochastics (WIAS), 2019. Disponível em: https://www.iccopt2019.berlin/downloads/ICCOPT2019_Conference_Book.pdf. Acesso em: 21 jan. 2026. -
APA
Birgin, E. J. G., & Martínez, J. M. (2019). A Newton-like method with mixed factorizations and cubic regularization and its usage in an Augmented Lagrangian framework. In Conference book. Berlin: Weierstrass Institute for Applied Analysis and Stochastics (WIAS). Recuperado de https://www.iccopt2019.berlin/downloads/ICCOPT2019_Conference_Book.pdf -
NLM
Birgin EJG, Martínez JM. A Newton-like method with mixed factorizations and cubic regularization and its usage in an Augmented Lagrangian framework [Internet]. Conference book. 2019 ;[citado 2026 jan. 21 ] Available from: https://www.iccopt2019.berlin/downloads/ICCOPT2019_Conference_Book.pdf -
Vancouver
Birgin EJG, Martínez JM. A Newton-like method with mixed factorizations and cubic regularization and its usage in an Augmented Lagrangian framework [Internet]. Conference book. 2019 ;[citado 2026 jan. 21 ] Available from: https://www.iccopt2019.berlin/downloads/ICCOPT2019_Conference_Book.pdf - Evaluating bound-constrained minimization software
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- A PDE-informed optimization algorithm for river flow predictions
- A concise and friendly introduction to the analysis of algorithms for continuous nonlinear optimization
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- On regularization and active-set methods with complexity for constrained optimization
- Second-order negative-curvature methods for box-constrained and general constrained optimization
- Augmented Lagrangian method with nonmonotone penalty parameters for constrained optimization
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