Block-Coordinate Gradient Descent Method for Linearly Constrained Nonsmooth Separable Optimization
نویسندگان
چکیده
منابع مشابه
A coordinate gradient descent method for nonsmooth separable minimization
This is a talk given at ISMP, Jul 31 2006.
متن کاملA coordinate gradient descent method for linearly constrained smooth optimization and support vector machines training
Support vector machines (SVMs) training may be posed as a large quadratic program (QP) with bound constraints and a single linear equality constraint. We propose a (block) coordinate gradient descent method for solving this problem and, more generally, linearly constrained smooth optimization. Our method is closely related to decomposition methods currently popular for SVM training. We establis...
متن کاملA block coordinate gradient descent method for regularized convex separable optimization and covariance selection
We consider a class of unconstrained nonsmooth convex optimization problems, in which the objective function is the sum of a convex smooth function on an open subset of matrices and a separable convex function on a set of matrices. This problem includes the covariance selection estimation problem that can be expressed as an `1-penalized maximum likelihood estimation problem. In this paper, we p...
متن کاملA Coordinate Gradient Descent Method for Nonsmooth Nonseparable Minimization
This paper presents a coordinate gradient descent approach for minimizing the sum of a smooth function and a nonseparable convex function. We find a search direction by solving a subproblem obtained by a second-order approximation of the smooth function and adding a separable convex function. Under a local Lipschitzian error bound assumption, we show that the algorithm possesses global and loca...
متن کاملRandom Block Coordinate Descent Methods for Linearly Constrained Optimization over Networks
In this paper we develop random block coordinate descent methods for minimizing large-scale linearly constrained convex problems over networks. Since coupled constraints appear in the problem, we devise an algorithm that updates in parallel at each iteration at least two random components of the solution, chosen according to a given probability distribution. Those computations can be performed ...
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 Optimization Theory and Applications
سال: 2008
ISSN: 0022-3239,1573-2878
DOI: 10.1007/s10957-008-9458-3