PreprintCC BY 4.0arXiv
An Adaptive Multi-Parameter ADMM Algorithm for Embedded MPC
Abstract
We introduce an adaptive multi-parameter variant of ADMM and prove that it exhibits local superlinear convergence once the set of active constraints has been identified. In simulations, the proposed algorithm consistently outperforms OSQP, a standard ADMM solver, in terms of iteration count. We then implement an MPC solver based on our method and compare its runtime against a broader selection of state-of-the-art algorithms. Evaluations on challenging benchmark problems reveal that our approach delivers competitive performance both in terms of average and worst-case solve times, without being limited to coarse tolerances, as is typically the case for standard ADMM implementations and first-order methods.
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BibTeX
@misc{zaupa2026adaptive,
title = {{An Adaptive Multi-Parameter ADMM Algorithm for Embedded MPC}},
author = {Alberto Zaupa and Mikael Johansson},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.12992},
eprint = {2609.12992},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.12992},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - An Adaptive Multi-Parameter ADMM Algorithm for Embedded MPC
AU - Alberto Zaupa
AU - Mikael Johansson
PY - 2026
DA - 2026/09/11
PB - arXiv
DO - 10.48550/arxiv.2609.12992
UR - https://arxiv.org/abs/2609.12992
AB - We introduce an adaptive multi-parameter variant of ADMM and prove that it exhibits local superlinear convergence once the set of active constraints has been identified. In simulations, the proposed algorithm consistently outperforms OSQP, a standard ADMM solver, in terms of iteration count. We then implement an MPC solver based on our method and compare its runtime against a broader selection of state-of-the-art algorithms. Evaluations on challenging benchmark problems reveal that our approach delivers competitive performance both in terms of average and worst-case solve times, without being limited to coarse tolerances, as is typically the case for standard ADMM implementations and first-order methods.
ER -