Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Mathematics > Optimization and Control

arXiv:1910.13742 (math)
[Submitted on 30 Oct 2019 (v1), last revised 10 Jun 2022 (this version, v4)]

Title:Unifying mirror descent and dual averaging

Authors:Anatoli Juditsky, Joon Kwon, Éric Moulines
View PDF
Abstract:We introduce and analyze a new family of first-order optimization algorithms which generalizes and unifies both mirror descent and dual averaging. Within the framework of this family, we define new algorithms for constrained optimization that combines the advantages of mirror descent and dual averaging. Our preliminary simulation study shows that these new algorithms significantly outperform available methods in some situations.
Subjects: Optimization and Control (math.OC); Machine Learning (cs.LG)
Cite as: arXiv:1910.13742 [math.OC]
  (or arXiv:1910.13742v4 [math.OC] for this version)
  https://doi.org/10.48550/arXiv.1910.13742

Submission history

From: Joon Kwon [view email]
[v1] Wed, 30 Oct 2019 09:55:25 UTC (32 KB)
[v2] Tue, 29 Sep 2020 14:21:56 UTC (158 KB)
[v3] Fri, 17 Dec 2021 14:47:50 UTC (241 KB)
[v4] Fri, 10 Jun 2022 11:40:40 UTC (257 KB)
Full-text links:

Access Paper:

view license
Current browse context:
math.OC
< prev   |   next >
new | recent | 2019-10
Change to browse by:
cs
cs.LG
math

References & Citations

export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)