First-Order Primal-Dual Method for Nonlinear Convex Cone Programming

Abstract

Nonlinear Convex Cone Programming (NCCP) problems are important and have many practical applications. In this paper, we introduces a flexible first-order primal-dual algorithm called the Variant Auxiliary Problem Principle (VAPP) for solving NCCP problems when the objective function and constraints are smooth and may be nonsmooth. Each iteration of VAPP generates a nonlinear approximation to the primal problem of an augmented Lagrangian method. The approximation incorporates both linearization and a variable distance-like function, and then the iterations of VAPP provide one decomposition property for NCCP. Motivated by recent applications in big data analysis, there has been an explosive growth in interest in the convergence rate analysis of parallel computing algorithms for large scale optimization problem. This paper proposes an iteration-based error bound and linear convergence of VAPP. Some verifiable sufficient conditions of this error bound are also discussed. For the general convex case (without error bound), we establish $O(1/t)$ convergence rate for primal suboptimality, feasibility and dual suboptimality. By adaptively setting in parameters at different iterations, we show an $O(1/t^2)$ rate for the strongly convex case. We further present Forward-Backward Splitting (FBS) formulation of VAPP method and establish the connection between VAPP and other primal-dual splitting methods. Finally, we discuss some issues in the implementation of VAPP.