A first-order inexact primal-dual algorithm for a class of convex-concave saddle point problems

Fan Jiang, Zhongming Wu, Xingju Cai, Hongchao Zhang

Abstract: In this paper, we study a first-order inexact primal-dual algorithm (I-PDA) for solving a class of convex-concave saddle point problems. The I-PDA, which involves a relative error criterion and generalizes the classical PDA, has the advantage of solving one subproblem inexactly when it does not have a closed-form solution. We show that the whole sequence generated by I-PDA converges to a saddle point solution with O ( 1 / N ) $\mathcal {O}(1/N)$ ergodic convergence rate, where N is the iteration number. In addition, under a mild calmness condition, we establish the global Q-linear convergence rate of the distance between the iterates generated by I-PDA and the solution set, and the R-linear convergence speed of the nonergodic iterates. Furthermore, we demonstrate that many problems arising from practical applications satisfy this calmness condition. Finally, some numerical experiments are performed to show the superiority and linear convergence behaviors of I-PDA.