NeurIPS 2024spotlight1 citations
Optimization Algorithm Design via Electric Circuits
Stephen P. Boyd, Tetiana Parshakova, Ernest K. Ryu, Jaewook J. Suh
Abstract
We present a novel methodology for convex optimization algorithm design using ideas from electric RLC circuits. Given an optimization problem, the first stage of the methodology is to design an appropriate electric circuit whose continuous-time dynamics converge to the solution of the optimization problem at hand. Then, the second stage is an automated, computer-assisted discretization of the continuous-time dynamics, yielding a provably convergent discrete-time algorithm. Our methodology recovers many classical (distributed) optimization algorithms and enables users to quickly design and explore a wide range of new algorithms with convergence guarantees.
Convex optimizationDistributed optimizationDecentralized optimizationADMMAlternating direction method of multipliersPG-EXTRAPerformance estimation problemContinuous-time analysisfirst-order optimizationproximal methods
BibTeX
@inproceedings{
boyd2024optimization,
title={Optimization Algorithm Design via Electric Circuits},
author={Stephen P. Boyd and Tetiana Parshakova and Ernest K. Ryu and Jaewook J. Suh},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=9Jmt1eER9P}
}