Randomized Primal-Dual Methods with Adaptive Step Sizes

Understand

In this paper we propose a class of randomized primal-dual methods to contend with large-scale saddle point problems defined by a convex-concave function $\mathcal{L}(\mathbf{x},y)\triangleq\sum_{i=1}^m f_i(x_i)+\Phi(\mathbf{x},y)-h(y)$.

• We analyze the convergence rate of the proposed method under mere convexity and strong convexity assumptions of $\mathcal{L}$ in $\mathbf{x}$-variable.
• In particular, assuming $\nabla_y\Phi(\cdot,\cdot)$ is Lipschitz and $\nabla_\mathbf{x}\Phi(\cdot,y)$ is coordinate-wise Lipschitz for any fixed $y$, the ergodic sequence generated by the algorithm achieves the convergence rate of $\mathcal{O}(M/k)$ in the expected primal-dual gap.
• Furthermore, assuming that $\mathcal{L}(\cdot,y)$ is strongly convex for any $y$, and that $\Phi(\mathbf{x},\cdot)$ is affine for any $\mathbf{x}$, the scheme enjoys a faster rate of $\mathcal{O}(M/k^2)$ in terms of primal solution suboptimality.