Augmented Lagrangian method for nonlinear circular conic programs: a local convergence analysis

Yue Lu, Hong Min Ma, Dong Yang Xue, Jein Shan Chen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we analyse a local convergence of augmented Lagrangian method (ALM) for a class of nonlinear circular conic optimization problems. In light of the singular value decomposition, the Debreu theorem and the implicit function theorem, we prove that the sequence generated by ALM converges to a local minimizer in the linear convergence rate under the constraint nondegeneracy condition and the strong second-order sufficient condition, in which the ratio constant is proportional to (Formula presented.), where τ is the associated penalty parameter with a given lower threshold. As a byproduct, we also derive explicit expressions of critical cone and its affine hull for the given nonlinear circular conic program.

Original languageEnglish
JournalOptimization
DOIs
Publication statusAccepted/In press - 2024

Keywords

  • augmented Lagrangian method
  • Circular cone
  • local convergence

ASJC Scopus subject areas

  • Control and Optimization
  • Management Science and Operations Research
  • Applied Mathematics

Fingerprint

Dive into the research topics of 'Augmented Lagrangian method for nonlinear circular conic programs: a local convergence analysis'. Together they form a unique fingerprint.

Cite this