Stochastic Primal–Dual Hybrid Gradient Algorithm with Adaptive Step Sizes
Antonin Chambolle, Claire Delplancke, Matthias J. Ehrhardt, Carola‐Bibiane Schönlieb, Junqi Tang
Abstract
In this work, we propose a new primal-dual algorithm with adaptive step sizes. The stochastic primal-dual hybrid gradient (SPDHG) algorithm with constant step sizes has become widely applied in large-scale convex optimization across many scientific fields due to its scalability. While the product of the primal and dual step sizes is subject to an upper-bound in order to ensure convergence, the selection of the ratio of the step sizes is critical in applications. Up-to-now there is no systematic and successful way of selecting the primal and dual step sizes for SPDHG. In this work, we propose a general class of adaptive SPDHG (A-SPDHG) algorithms and prove their convergence under weak assumptions. We also propose concrete parameters-updating strategies which satisfy the assumptions of our theory and thereby lead to convergent algorithms. Numerical examples on computed tomography demonstrate the effectiveness of the proposed schemes.