A Stochastic Gradient Method with an Exponential Convergence Rate for Finite Training Sets

The notable changes over the current version: - worked example of convergence rates showing SAG can be faster than first-order methods - pointing out that the storage cost is O(n) for linear models - the more-stable line-search - comparison to additional optimal SG methods - comparison to rates of coordinate descent methods in quadratic case.

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Additional Info

Field Value
Source NIPS'12 - 26 th Annual Conference on Neural Information Processing Systems (2012)
Author Le Roux, Nicolas, Schmidt, Mark, Bach, Francis
Maintainer CCSD
Last Updated May 13, 2026, 02:48 (UTC)
Created May 13, 2026, 02:48 (UTC)
Identifier hal-00674995
Language en
Rights https://about.hal.science/hal-authorisation-v1/
contributor Statistical Machine Learning and Parsimony (SIERRA) ; Département d'informatique - ENS-PSL (DI-ENS) ; École normale supérieure - Paris (ENS-PSL) ; Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL) ; Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Paris-Rocquencourt ; Institut National de Recherche en Informatique et en Automatique (Inria)
coverage Lake Tahoe, United States
creator Le Roux, Nicolas
date 2012-05-13T00:00:00
harvest_object_id 8087ecc9-f67c-4000-8e12-c800a35b88fb
harvest_source_id 3374d638-d20b-4672-ba96-a23232d55657
harvest_source_title test moissonnage SELUNE
metadata_modified 2025-10-24T00:00:00
relation info:eu-repo/semantics/altIdentifier/arxiv/1202.6258
set_spec type:COMM