Rescaling and stepsize selection in proximal methods using separable generalized distances

SIAM Journal on Optimization

Volumn 12, Issue 1, 2002, Pages 238-261

  Da Silva E Silva, Paulo José ^a   Eckstein, Jonathan ^a   Humes Jr , Carlos ^a  

^a UNIVERSITY OF SÃO PAULO   (Brazil)

Author keywords

divergence; Bregman distances; Convex programming; Proximal algorithms; Variational inequalities

Indexed keywords

EID: 0036342099     PISSN: 10526234     EISSN: None     Source Type: Journal    
DOI: 10.1137/S1052623499365784     Document Type: Article

References (32)

1. An interior-proximal method for convex linearly constrained problems and its extension to variational problems
2. A logarithmic-quadratic proximal method for variational inequalities
3. Interior proximal and multiplier methods based on second order homogeneous kernels
4. Lagrangian duality and related multiplier methods for variational inequality problems
5. Penalty/barrier multiplier methods for convex programming problems
6. The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming
7. An interior-point method with Bregman functions for the variational inequality problem with paramonotone operators
8. An iterative row-action method for interval convex programming
9. The proximal minimization algorithms with D-functions
10. Convergence analysis of a proximal-like minimization algorithm using Bregman functions
11. Nonlinear proximal point algorithms using Bregman functions, with applications to convex programming
12. Approximate iterations in Bregman-function-based proximal algorithms
13. A practical general approximation criterion for methods of multipliers based on Bregman distances
14. A necessary and sufficient condition to have bounded multipliers in nonconvex programming
15. Strict convex regularizations, proximal point and augmented Lagrangians
16. Augmented Lagrangian methods and proximal points methods for convex optimization
17. Entropy-like proximal methods in convex programming
18. On the twice differentiable cubic augmented Lagrangian
19. Proximal minimization methods with generalized Bregman functions
20. Introduction to optimization
21. Extension of Fenchel's duality theorem for convex functions
22. Augmented Lagrangians and applications of the proximal point algorithm in convex programming
23. An inexact hybrid generalized proximal point algorithm and some new results on the theory of Bregman functions
24. Entropic proximal mappings with applications to nonlinear programming
25. Convergence of proximal-like algorithms
26. On the convergence of the exponential multiplier method for convex programming