Consensus Planning with Primal, Dual, and Proximal Agents

TL;DR

This paper introduces a versatile consensus planning algorithm that coordinates heterogeneous agents with primal, dual, and proximal interfaces, ensuring convergence and practical applicability in complex, real-world systems.

Contribution

It extends consensus algorithms to handle mixed agent types, combining ADMM-like, dual ascent, and linearized ADMM updates, with proven convergence guarantees.

Findings

The algorithm converges with a sublinear O(1/k) rate under mild conditions.

It achieves two-step linear convergence under stronger assumptions.

Empirical results demonstrate practical effectiveness.

Abstract

Consensus planning is a method for coordinating decision making across complex systems and organizations, including complex supply chain optimization pipelines. It arises when large interdependent distributed agents (systems) share common resources and must act in order to achieve a joint goal. In prior consensus planning work, all agents have been assumed to have the same interaction pattern (e.g., all dual agents or all primal agents or all proximal agents), most commonly using the Alternating Direction Method of Multipliers (ADMM) as proximal agents. However, this is often not a valid assumption in practice, where agents consist of large complex systems, and where we might not have the luxury of modifying these large complex systems at will. In this paper, we introduce a consensus algorithm that overcomes this hurdle by allowing for the coordination of agents with different types of interfaces (named primal, dual, and proximal). Our consensus planning algorithm allows for any mix of agents by combining ADMM-like updates for the proximal agents, dual ascent updates for the dual agents, and linearized ADMM updates for the primal agents. We prove convergence results for the algorithm, namely a sublinear O(1/k) convergence rate under mild assumptions, and two-step linear convergence under stronger assumptions. We also discuss enhancements to the basic method and provide illustrative empirical results.