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Exact augmented Lagrangian functions for nonlinear semidefinite programming

Ellen H. Fukuda(ellen***at***i.kyoto-u.ac.jp)
Bruno F. Lourenco(lourenco***at***st.seikei.ac.jp)

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.

Keywords: Differentiable exact merit functions, generalized augmented Lagrangian functions, nonlinear semidefinite programming

Category 1: Linear, Cone and Semidefinite Programming (Semi-definite Programming )

Category 2: Nonlinear Optimization (Constrained Nonlinear Optimization )

Citation: Kyoto University and Seikei University, May/2017

Download: [PDF]

Entry Submitted: 05/18/2017
Entry Accepted: 05/18/2017
Entry Last Modified: 05/18/2017

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