发表机构
KTH Royal Institute of Technology（皇家理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种自适应多参数ADMM算法，证明其局部超线性收敛，并在嵌入式MPC中实现，相比OSQP等算法在迭代次数和求解时间上更具竞争力。
AI 中文摘要
我们提出了一种自适应多参数的ADMM变体，并证明了在识别出有效约束集后，该算法具有局部超线性收敛性。在仿真中，所提出的算法在迭代次数方面始终优于标准ADMM求解器OSQP。随后，我们实现了一个基于我们方法的MPC求解器，并将其运行时间与更广泛的最先进算法进行了比较。在具有挑战性的基准问题上的评估表明，我们的方法在平均和最坏情况求解时间方面均具有竞争力的性能，且不像标准ADMM实现和一阶方法那样通常局限于粗糙的容差。
英文摘要
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.
CommentsAccepted for publication at CDC 2026
