TurboADMM: A Structure-Exploiting Parallel Solver for Multi-Agent Trajectory Optimization

TL;DR

TurboADMM is a novel, structure-exploiting parallel solver for multi-agent trajectory optimization that achieves near-linear scalability by combining agent decomposition, temporal warmstarting, and hotstarting techniques.

Contribution

The paper introduces TurboADMM, a specialized QP solver that leverages problem structure for scalable multi-agent trajectory optimization, outperforming general-purpose solvers.

Findings

Achieves near linear complexity with respect to agent count.

Effectively exploits temporal and agent decomposition structures.

Demonstrates improved scalability over existing solvers.

Abstract

Multi-agent trajectory optimization with dense interaction networks require solving large coupled QPs at control rates, yet existing solvers fail to simultaneously exploit temporal structure, agent decomposition, and iteration similarity. One usually treats multi-agent problems monolithically when using general-purpose QP solvers (OSQP, MOSEK), which encounter scalability difficulties with agent count. Structure-exploiting solvers (HPIPM) leverage temporal structure through Riccati recursion but can be vulnerable to dense coupling constraints. We introduce TurboADMM, a specialized single-machine QP solver that achieves empirically near linear complexity in agent count through systematic co-design of three complementary components: (1) ADMM decomposition creates per-agent subproblems solvable in parallel, preserving block-tridiagonal structure under dense coupling; (2) Riccati warmstart exploits temporal structure to provide high-quality primal-dual initialization for each agent's QP; (3) parametric QP hotstart \footnote{In the paper, we refer warmstart as the technique that uses the Riccati equation results as auxiliary QP initialization for a single QP solve, while hotstart as reusing the QR factorization across QP solve iterations.}in qpOASES reuses similar KKT system factorizations across ADMM iterations.