@inproceedings{1f1afab2a1274eb181706863e173ef8e,
title = "A Robust Accelerated Optimization Algorithm for Strongly Convex Functions",
abstract = "This work proposes an accelerated first-order algorithm we call the Robust Momentum Method for optimizing smooth strongly convex functions. The algorithm has a single scalar parameter that can be tuned to trade off robustness to gradient noise versus worst-case convergence rate. At one extreme, the algorithm is faster than Nesterov's Fast Gradient Method by a constant factor but more fragile to noise. At the other extreme, the algorithm reduces to the Gradient Method and is very robust to noise. The algorithm design technique is inspired by methods from classical control theory and the resulting algorithm has a simple analytical form. Algorithm performance is verified on a series of numerical simulations in both noise-free and relative gradient noise cases.",
author = "Saman Cyrus and Bin Hu and {Van Scoy}, Bryan and Laurent Lessard",
note = "Funding Information: 1S. Cyrus and L. Lessard are with the Department of Electrical and Computer Engineering, University of Wisconsin–Madison. 2All authors are with the Wisconsin Institute for Discovery at the University of Wisconsin–Madison. Email addresses: {cyrus2,bhu38,vanscoy,laurent.lessard}@wisc.edu 3This material is based upon work supported by the National Science Foundation under Grants No. 1656951 and 1750162. Publisher Copyright: {\textcopyright} 2018 AACC.; 2018 Annual American Control Conference, ACC 2018 ; Conference date: 27-06-2018 Through 29-06-2018",
year = "2018",
month = aug,
day = "9",
doi = "10.23919/ACC.2018.8430824",
language = "English (US)",
isbn = "9781538654286",
series = "Proceedings of the American Control Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1376--1381",
booktitle = "2018 Annual American Control Conference, ACC 2018",
address = "United States",
}