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Optimality conditions and global convergence for nonlinear semidefinite programming

Abstract

Sequential optimality conditions have played a major role in unifying and extending global convergence results for several classes of algorithms for general nonlinear optimization. In this paper, we extend theses concepts for nonlinear semidefinite programming. We define two sequential optimality conditions for nonlinear semidefinite programming. The first is a natural extension of the so-called Approximate-Karush–Kuhn–Tucker (AKKT), well known in nonlinear optimization. The second one, called Trace-AKKT, is more natural in the context of semidefinite programming as the computation of eigenvalues is avoided. We propose an augmented Lagrangian algorithm that generates these types of sequences and new constraint qualifications are proposed, weaker than previously considered ones, which are sufficient for the global convergence of the algorithm to a stationary point.

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Correspondence to Gabriel Haeser.

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This work was supported by FAPESP (Grants 2013/05475-7 and 2017/18308-2), CNPq and CAPES.

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Andreani, R., Haeser, G. & Viana, D.S. Optimality conditions and global convergence for nonlinear semidefinite programming. Math. Program. 180, 203–235 (2020). https://doi.org/10.1007/s10107-018-1354-5

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Keywords

  • Nonlinear semidefinite programming
  • Optimality conditions
  • Constraint qualifications
  • Practical algorithms

Mathematics Subject Classification

  • 90C22
  • 90C46
  • 90C30