Exact augmented Lagrangian functions for nonlinear semidefinite programming

Ellen H. Fukuda () and Bruno F. Lourenço ()
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Ellen H. Fukuda: Kyoto University
Bruno F. Lourenço: University of Tokyo

Computational Optimization and Applications, 2018, vol. 71, issue 2, No 7, 457-482

Abstract: Abstract In this paper, we study augmented Lagrangian functions for nonlinear semidefinite programming (NSDP) problems with exactness properties. The term exact is used in the sense that the penalty parameter can be taken appropriately, so a single minimization of the augmented Lagrangian recovers a solution of the original problem. This leads to reformulations of NSDP problems into unconstrained nonlinear programming ones. Here, we first establish a unified framework for constructing these exact functions, generalizing Di Pillo and Lucidi’s work from 1996, that was aimed at solving nonlinear programming problems. Then, through our framework, we propose a practical augmented Lagrangian function for NSDP, proving that it is continuously differentiable and exact under the so-called nondegeneracy condition. We also present some preliminary numerical experiments.

Keywords: Differentiable exact merit functions; Generalized augmented Lagrangian functions; Nonlinear semidefinite programming (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10589-018-0017-z

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