pith. sign in

arxiv: 1702.06751 · v1 · pith:CVSYVV4Snew · submitted 2017-02-22 · 🧮 math.OC

Integration Methods and Accelerated Optimization Algorithms

classification 🧮 math.OC
keywords equationintegrationaccelerateddifferentialflowgradientmethodsmulti-step
0
0 comments X
read the original abstract

We show that accelerated optimization methods can be seen as particular instances of multi-step integration schemes from numerical analysis, applied to the gradient flow equation. In comparison with recent advances in this vein, the differential equation considered here is the basic gradient flow and we show that multi-step schemes allow integration of this differential equation using larger step sizes, thus intuitively explaining acceleration results.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Adaptive Federated Optimization

    cs.LG 2020-02 unverdicted novelty 6.0

    Proposes federated adaptive optimizers (FedAdagrad, FedAdam, FedYogi) with convergence analysis for non-convex objectives under data heterogeneity and reports empirical gains over FedAvg.