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DOI: 10.4230/LIPIcs.ITCS.2017.2
URN: urn:nbn:de:0030-drops-81640
URL: http://drops.dagstuhl.de/opus/volltexte/2017/8164/
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Panageas, Ioannis ; Piliouras, Georgios

Gradient Descent Only Converges to Minimizers: Non-Isolated Critical Points and Invariant Regions

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LIPIcs-ITCS-2017-2.pdf (0.7 MB)


Abstract

Given a twice continuously differentiable cost function f, we prove that the set of initial conditions so that gradient descent converges to saddle points where \nabla^2 f has at least one strictly negative eigenvalue, has (Lebesgue) measure zero, even for cost functions f with non-isolated critical points, answering an open question in [Lee, Simchowitz, Jordan, Recht, COLT 2016]. Moreover, this result extends to forward-invariant convex subspaces, allowing for weak (non-globally Lipschitz) smoothness assumptions. Finally, we produce an upper bound on the allowable step-size.

BibTeX - Entry

@InProceedings{panageas_et_al:LIPIcs:2017:8164,
  author =	{Ioannis Panageas and Georgios Piliouras},
  title =	{{Gradient Descent Only Converges to Minimizers: Non-Isolated Critical Points and Invariant Regions}},
  booktitle =	{8th Innovations in Theoretical Computer Science Conference (ITCS 2017)},
  pages =	{2:1--2:12},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-029-3},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{67},
  editor =	{Christos H. Papadimitriou},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2017/8164},
  URN =		{urn:nbn:de:0030-drops-81640},
  doi =		{10.4230/LIPIcs.ITCS.2017.2},
  annote =	{Keywords: Gradient Descent, Center-stable manifold, Saddle points, Hessian}
}

Keywords: Gradient Descent, Center-stable manifold, Saddle points, Hessian
Seminar: 8th Innovations in Theoretical Computer Science Conference (ITCS 2017)
Issue Date: 2017
Date of publication: 24.11.2017


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