Proximal-Based Pre-correction Decomposition Methods for Structured Convex Minimization Problems
نویسندگان
چکیده
منابع مشابه
A proximal-based decomposition method for convex minimization problems
This paper presents a decomposition method for solving convex minimization problems. At each iteration, the algorithm computes two proximal steps in the dual variables and one proximal step in the primal variables. We derive this algorithm from Rockafellar's proximal method of multipliers, which involves an augmented Lagrangian with an additional quadratic proximal term. The algorithm preserves...
متن کاملConvex risk minimization via proximal splitting methods
In this paper we investigate the applicability of a recently introduced primal-dual splitting method in the context of solving portfolio optimization problems which assume the minimization of risk measures associated to different convex utility functions. We show that, due to the splitting characteristic of the used primal-dual method, the main effort in implementing it constitutes in the calcu...
متن کاملA Bundle Interior Proximal Method for Solving Convex Minimization Problems
In this paper we extend the standard bundle proximal method for finding the minimum of a convex not necessarily differentiable function on the nonnegative orthant. The strategy consists in approximating the objective function by a piecewise linear convex function and using distance–like functions based on second order homogeneous kernels. First we prove the convergence of this new bundle interi...
متن کاملA Proximal Decomposition Method for Solving Convex Variational Inverse Problems
A broad range of inverse problems can be abstracted into the problem of minimizing the sum of several convex functions in a Hilbert space. We propose a proximal decomposition algorithm for solving this problem with an arbitrary number of nonsmooth functions and establish its convergence. The algorithm fully decomposes the problem in that it involves each function individually via its own proxim...
متن کاملEntropic proximal decomposition methods for convex programs and variational inequalities
We consider convex optimization and variational inequality problems with a given separable structure. We propose a new decomposition method for these problems which combines the recent logarithmicquadratic proximal theory introduced by the authors with a decomposition method given by Chen-Teboulle for convex problems with particular structure. The resulting method allows to produce for the firs...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of the Operations Research Society of China
سال: 2014
ISSN: 2194-668X,2194-6698
DOI: 10.1007/s40305-014-0042-2