Fast proximal algorithms for nonsmooth convex optimization
نویسندگان
چکیده
منابع مشابه
Incremental Constraint Projection-Proximal Methods for Nonsmooth Convex Optimization
We consider convex optimization problems with structures that are suitable for stochastic sampling. In particular, we focus on problems where the objective function is an expected value or is a sum of a large number of component functions, and the constraint set is the intersection of a large number of simpler sets. We propose an algorithmic framework for projection-proximal methods using rando...
متن کاملProximal point algorithms for nonsmooth convex optimization with fixed point constraints
The problem of minimizing the sum of nonsmooth, convex objective functions defined on a real Hilbert space over the intersection of fixed point sets of nonexpansive mappings, onto which the projections cannot be efficiently computed, is considered. The use of proximal point algorithms that use the proximity operators of the objective functions and incremental optimization techniques is proposed...
متن کاملSmooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization
We propose a new randomized coordinate descent method for a convex optimization template with broad applications. Our analysis relies on a novel combination of four ideas applied to the primal-dual gap function: smoothing, acceleration, homotopy, and coordinate descent with non-uniform sampling. As a result, our method features the first convergence rate guarantees among the coordinate descent ...
متن کاملFast Multiple-Splitting Algorithms for Convex Optimization
Abstract. We present in this paper two different classes of general K-splitting algorithms for solving finite-dimensional convex optimization problems. Under the assumption that the function being minimized has a Lipschitz continuous gradient, we prove that the number of iterations needed by the first class of algorithms to obtain an ε-optimal solution is O(1/ε). The algorithms in the second cl...
متن کاملStochastic Coordinate Descent for Nonsmooth Convex Optimization
Stochastic coordinate descent, due to its practicality and efficiency, is increasingly popular in machine learning and signal processing communities as it has proven successful in several large-scale optimization problems , such as l1 regularized regression, Support Vector Machine, to name a few. In this paper, we consider a composite problem where the nonsmoothness has a general structure that...
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: Operations Research Letters
سال: 2020
ISSN: 0167-6377
DOI: 10.1016/j.orl.2020.09.008