Distributed Computing for Huge-Scale Aggregative Convex Programming

TL;DR

This paper introduces a distributed algorithm for large-scale aggregative convex programming problems with linear, quadratic, and affine constraints, ensuring convergence and efficiency.

Contribution

The paper develops a novel distributed algorithm combining consensus, slack variables, and ADMM for large-scale convex optimization with proven convergence.

Findings

Algorithm converges to optimal solutions under feasibility conditions.

Convergence rate estimated at O(1/√k).

Effective partitioning of constraints and variables enhances distributed computation.

Abstract

Concerning huge-scale aggregative convex programming of a linear objective subject to the affine constraints of equality and inequality and the quadratic constraints of inequality, convex and aggregatively computable, an algorithm is developed for its distributed computing. The consensus with single common variable is used to partition the constraints into multi-consensus blocks, and the subblocks of each consensus block are employed to partition the primal variables into multiple sets of disjoint subvectors. The global consensus constraints of equality and the original constraints are converted into the extended constraints of equality via slack variables to help resolve initialization of the algorithm. The augmented Lagrangian, the block-coordinate Gauss-Seidel method, the proximal point method with double proximal terms or single, and ADMM are used to update the primal and slack variables; descent models with built-in bounds are used to update the dual. The feasibility conditions for the algorithm to produce optimal solutions are described and their realizations through initial and parameter values are outlined. Under the feasibility supposed, convergence of the algorithm to optimal solutions is argued and the rate of convergence, $O(1/k^{1/2})$ is estimated. Issues requiring further explorations are listed.