Excessive Gap Technique in Nonsmooth Convex Minimization 论文
2005SIAM Journal on Optimization引用 250
Stochastic Gradient Optimization TechniquesSparse and Compressive Sensing TechniquesComplexity and Algorithms in Graphs
摘要
In this paper we introduce a new primal-dual technique for convergence analysis of gradient schemes for nonsmooth convex optimization. As an example of its application, we derive a primal-dual gradient method for a special class of structured nonsmooth optimization problems, which ensures a rate of convergence of order O(1/k), where k is the iteration count. Another example is a gradient scheme, which minimizes a nonsmooth strongly convex function with known structure with rate of convergence O(1/k2 ). In both cases the efficiency of the methods is higher than the corresponding black-box lower complexity bounds by an order of magnitude.