Repository logo

Infoscience

  • English
  • French
Log In
Logo EPFL, École polytechnique fédérale de Lausanne

Infoscience

  • English
  • French
Log In
  1. Home
  2. Academic and Research Output
  3. Conferences, Workshops, Symposiums, and Seminars
  4. An Inexact Augmented Lagrangian Framework for Nonconvex Optimization with Nonlinear Constraints
 
conference paper

An Inexact Augmented Lagrangian Framework for Nonconvex Optimization with Nonlinear Constraints

Sahin, Mehmet Fatih  
•
Eftekhari, Armin  
•
Alacaoglu, Ahmet  
Show more
2019
[Proceedings of NEURips 2019]
NeurIPS 2019 : Thirty-third Conference on Neural Information Processing Systems

We propose a practical inexact augmented Lagrangian method (iALM) for nonconvex problems with nonlinear constraints. We characterize the total computational complexity of our method subject to a verifiable geometric condition, which is closely related to the Polyak-Lojasiewicz and Mangasarian-Fromowitz conditions. 000278535 520__ $$aIn particular, when a first-order solver is used for the inner iterates, we prove that iALM finds a first-order stationary point with (O) over tilde (1/epsilon(3)) calls to the first-order oracle. If, in addition, the problem is smooth and a second-order solver is used for the inner iterates, iALM finds a second-order stationary point with (O) over tilde (1/epsilon(5)) calls to the second-order oracle. These complexity results match the known theoretical results in the literature.

  • Files
  • Details
  • Metrics
Loading...
Thumbnail Image
Name

Ial_Neurips.pdf

Access type

openaccess

Size

832.24 KB

Format

Adobe PDF

Checksum (MD5)

96081000a53eaab9d256007cdb9dbe48

Logo EPFL, École polytechnique fédérale de Lausanne
  • Contact
  • infoscience@epfl.ch

  • Follow us on Facebook
  • Follow us on Instagram
  • Follow us on LinkedIn
  • Follow us on X
  • Follow us on Youtube
AccessibilityLegal noticePrivacy policyCookie settingsEnd User AgreementGet helpFeedback

Infoscience is a service managed and provided by the Library and IT Services of EPFL. © EPFL, tous droits réservés