On the twice differentiable cubic augmented Lagrangian

DOI10.1007/BF02192031zbMath0842.90093OpenAlexW2043177638MaRDI QIDQ1908654

Krzysztof C. Kiwiel

Publication date: 4 March 1996

Published in: Journal of Optimization Theory and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf02192031

zbMATH Keywords

multiplier methodsproximal methodsBregman functionsinequality constrained minimizationtwice differentiablecubic LagrangianRockafellar's quadratic augmented Lagrangian

Mathematics Subject Classification ID

Convex programming (90C25)

Related Items

Separation approach for augmented lagrangians in Constrained nonconvex optimization, Saddle points of general augmented Lagrangians for constrained nonconvex optimization, Augmented Lagrangian functions for cone constrained optimization: the existence of global saddle points and exact penalty property, Double-regularization proximal methods, with complementarity applications, The global convergence of augmented Lagrangian methods based on NCP function in constrained nonconvex optimization, Approximate iterations in Bregman-function-based proximal algorithms

Cites Work

• Unnamed Item
• On the convergence of the exponential multiplier method for convex programming
• Entropic Proximal Mappings with Applications to Nonlinear Programming
• Combined Primal–Dual and Penalty Methods for Convex Programming
• Augmented Lagrangians and Applications of the Proximal Point Algorithm in Convex Programming
• Nonlinear Proximal Point Algorithms Using Bregman Functions, with Applications to Convex Programming
• A dual approach to solving nonlinear programming problems by unconstrained optimization