Fetching the paper…
Reading the bibliography…
We study the trade-offs between convergence rate and robustness to gradient errors in designing a first-order algorithm.
A stochastic approximation method
H. Robbins and S. Monro · 1951
Earlier work this paper cites.
Acceleration of stochastic approximation by averaging
B. T. Polyak and A. B. Juditsky · 1992
Earlier work this paper cites.
The robust
A. A. Stoorvogel · 1993
Earlier work this paper cites.
Parameter-dependent Lyapunov functions and the discrete-time popov criterion for robust analysis
W.M. Haddad and D.S. Bernstein · 1994
Earlier work this paper cites.
Polynomial roots from companion matrix eigenvalues
A. Edelman and H. Murakami · 1995
Earlier work this paper cites.
Kemin Zhou, John Comstock Doyle, Keith Glover, et al · 1996
Earlier work this paper cites.
Nonlinear programming
D.P Bertsekas · 1999
Earlier work this paper cites.
Introductory Lectures on Convex Optimization: A Basic Course
Yurii Nesterov · 2004
Earlier work this paper cites.
Smooth optimization with approximate gradient
A. d’Aspremont · 2008
Earlier work this paper cites.
Incremental gradient, subgradient, and proximal methods for convex optimization: A survey
D.P Bertsekas · 2011
Earlier work this paper cites.
Convergence rates of inexact proximal-gradient methods for convex optimization
M. Schmidt, Nicolas Le Roux, and F.R. Bach · 2011
Earlier work this paper cites.
Optimal stochastic approximation algorithms for strongly convex stochastic composite optimization I: A generic algorithmic framework
Saeed Ghadimi and Guanghui Lan · 2012
Earlier work this paper cites.
An optimal method for stochastic composite optimization
Guanghui Lan · 2012
Earlier work this paper cites.
Non-strongly-convex smooth stochastic approximation with convergence rate o(1/n)
Francis Bach and Eric Moulines · 2013
Earlier work this paper cites.
Intermediate gradient methods for smooth convex problems with inexact oracle
O. Devolder, F. Glineur, and Y. Nesterov · 2013
Earlier work this paper cites.
Exactness, inexactness and stochasticity in first-order methods for large-scale convex optimization
Olivier Devolder · 2013
Cited alongside, same era.
Optimal stochastic approximation algorithms for strongly convex stochastic composite optimization II: Shrinking procedures and optimal algorithms
Saeed Ghadimi and Guanghui Lan · 2013
Cited alongside, same era.
Combinatorial bounds and scaling laws for noise amplification in networks
Ali Jadbabaie and Alex Olshevsky · 2013
Cited alongside, same era.
Private empirical risk minimization: Efficient algorithms and tight error bounds
R. Bassily, A. Smith, and A. Thakurta · 2014
Cited alongside, same era.
First-order methods of smooth convex optimization with inexact oracle
O. Devolder, F. Glineur, and Y. Nesterov · 2014
Cited alongside, same era.
Bridging the gap between constant step size stochastic gradient descent and markov chains, 2017
Aymeric Dieuleveut, Alain Durmus, and Francis Bach · 2017
Later among the works it cites.
Couplings and quantitative contraction rates for Langevin dynamics
A. Eberle, A. Guillin, and R. Zimmer · 2017
Later among the works it cites.
Analysis of approximate stochastic gradient using quadratic constraints and sequential semidefinite programs, 2017
B. Hu, P. Seiler, and L. Lessard · 2017
Later among the works it cites.
Dissipativity theory for Nesterov’s accelerated method
Bin Hu and Laurent Lessard · 2017
Later among the works it cites.
Non-convex learning via stochastic gradient langevin dynamics: A nonasymptotic analysis
Maxim Raginsky, Alexander Rakhlin, and Matus Telgarsky · 2017
Later among the works it cites.
A robust accelerated optimization algorithm for strongly convex functions
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Michael Grant and Stephen Boyd · 2014
Cited alongside, same era.
Robustness versus acceleration, August 2014
M. Hardt · 2014
Cited alongside, same era.
The multidimensional n-th order heavy ball method and its application to extremum seeking
Simon Michalowsky and Christian Ebenbauer · 2014
Cited alongside, same era.
From averaging to acceleration, there is only a step-size
N. Flammarion and F. Bach · 2015
Cited alongside, same era.
Adaptive restart for accelerated gradient schemes
B. O’Donoghue and E. Candès · 2015
Cited alongside, same era.
Stochastic intermediate gradient method for convex problems with stochastic inexact oracle
Pavel Dvurechensky and Alexander Gasnikov · 2016
Cited alongside, same era.
On performance of consensus protocols subject to noise: Role of hitting times and network structure
Ali Jadbabaie and Alex Olshevsky · 2016
Cited alongside, same era.
S. Cyrus, B. Hu, B. Van Scoy, and L. Lessard · 2018
Closest in time.
Analysis of optimization algorithms via integral quadratic constraints: Nonstrongly convex problems
M. Fazlyab, A. Ribeiro, M. Morari, and V. Preciado · 2018
Closest in time.
Global Convergence of Stochastic Gradient Hamiltonian Monte Carlo for Non-Convex Stochastic Optimization: Non-Asymptotic Performance Bounds and Momentum-Based Acceleration, September 2018
X. Gao, M. Gürbüzbalaban, and L. Zhu · 2018
Closest in time.
Breaking reversibility accelerates langevin dynamics for global non-convex optimization
Xuefeng Gao, Mert Gürbüzbalaban, and Lingjiong Zhu · 2018
Closest in time.
Variance amplification of accelerated first-order algorithms for strongly convex quadratic optimization problems
Hesameddin Mohammadi, Meisam Razaviyayn, and Mihailo R Jovanović · 2018
Closest in time.
An explicit convergence rate for Nesterov’s method from sdp
Sam Safavi, Bikash Joshi, Guilherme França, and José Bento · 2018
Closest in time.
A universally optimal multistage accelerated stochastic gradient method
Necdet Serhat Aybat, Alireza Fallah, Mert Gürbüzbalaban, and Asuman Ozdaglar · 2019
Closest in time.
Accelerated linear convergence of stochastic momentum methods in Wasserstein distances
Bugra Can, Mert Gurbuzbalaban, and Lingjiong Zhu · 2019
Closest in time.
Accelerated Linear Convergence of Stochastic Momentum Methods in Wasserstein Distances
Bugra Can, Mert Gürbüzbalaban, and Lingjiong Zhu · 2019
Closest in time.