A primal-dual proximal point algorithm for constrained convex programs

DOI10.1016/j.amc.2003.12.137zbMath1063.65044OpenAlexW2028754765MaRDI QIDQ1763276

Abdelouahed Hamdi

Publication date: 22 February 2005

Published in: Applied Mathematics and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.amc.2003.12.137

zbMATH Keywords

multipliers methodproximal point methodBregman functionsquadratic regularizationconvex constrained minimization

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Convex programming (90C25)

Related Items (3)

Entropy-Like Minimization Methods Based On Modified Proximal Point Algorithm ⋮ Logarithmic quasi-distance proximal point scalarization method for multi-objective programming ⋮ Dual–primal proximal point algorithms for extended convex programming

Cites Work

• Implementing proximal point methods for linear programming
• Enlarging the region of convergence of Newton's method for constrained optimization
• Multiplier and gradient methods
• Applications of the method of partial inverses to convex programming: Decomposition
• Augmented Lagrangians and Applications of the Proximal Point Algorithm in Convex Programming
• Proximal Minimization Methods with Generalized Bregman Functions
• A Generalized Proximal Point Algorithm for the Variational Inequality Problem in a Hilbert Space
• Nonlinear Proximal Point Algorithms Using Bregman Functions, with Applications to Convex Programming
• PARTIAL PROXIMAL METHOD OF MULTIPLIERS FOR CONVEX PROGRAMMING PROBLEMS
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