Surpassing gradient descent provably: a cyclic incremental method with linear convergence rate

Aryan Mokhtari; Mert Gürbüzbalaban; Alejandro Ribeiro

doi:10.1137/16M1101702

Publications · Journal article · SIAM J. Optim. 2018

Surpassing gradient descent provably: a cyclic incremental method with linear convergence rate

Aryan Mokhtari, Mert Gürbüzbalaban, Alejandro Ribeiro

SIAM Journal on Optimization, 28(2), pp. 1420–1447, 2018.

Publisher (DOI)arXiv:1611.00347

Topics

Incremental methods & data ordering

Cite

@article{mokhtari2018cyclic,
  title   = {Surpassing gradient descent provably: a cyclic incremental method with linear convergence rate},
  author  = {Aryan Mokhtari and Mert Gürbüzbalaban and Alejandro Ribeiro},
  year    = {2018},
  journal = {SIAM Journal on Optimization},
  volume  = {28(2)},
  pages   = {1420--1447},
  doi     = {10.1137/16M1101702},
}

Relatedsame topics

Why random reshuffling beats stochastic gradient descent (Math. Program., 2021)
Randomness and permutations in coordinate descent methods (Math. Program., 2020)
Convergence rate of incremental gradient and incremental Newton methods (SIAM J. Optim., 2019)
Global convergence rate of proximal incremental aggregated gradient methods (SIAM J. Optim., 2018)
A double incremental aggregated gradient method with linear convergence rate for large-scale optimization (ICASSP, 2017)

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