A second-order convergence augmented Lagrangian method using non-quadratic penalty functions

M. D. Sánchez () and M. L. Schuverdt ()
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M. D. Sánchez: University of La Plata
M. L. Schuverdt: University of La Plata

OPSEARCH, 2019, vol. 56, issue 2, No 2, 390-408

Abstract: Abstract The purpose of the present paper is to study the global convergence of a practical Augmented Lagrangian model algorithm that considers non-quadratic Penalty–Lagrangian functions. We analyze the convergence of the model algorithm to points that satisfy the Karush–Kuhn–Tucker conditions and also the weak second-order necessary optimality condition. The generation scheme of the Penalty–Lagrangian functions includes the exponential penalty function and the logarithmic-barrier without using convex information.

Keywords: Nonlinear programming; Augmented Lagrangian methods; Global convergence; Constraint qualifications; Sequential optimality conditions (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s12597-019-00366-3

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