For a class of large-scale separable convex optimization problems,an adaptive stochastic primal-dual algorithm is proposed.The optimization problem is reformulated as a saddle point problem with separable dual variables.Then,the dual variables of the saddle point problem are randomly updated with adaptively selected step size.The adaptive stochastic primal-dual algorithm almost surely converges with rate O(1/N)in an ergodic sense.The results of numerical experiments indicate that the algorithm can effectively solve the problem of positron emission computed tomography.
关键词
大规模可分凸优化问题/随机优化/原始对偶算法/自适应步长
Key words
large scale separable convex optimization problem/stochastic optimization/primal-dual algorithms/adaptive step-size
维普
万方数据