Enhancing Sharp Augmented Lagrangian Methods with Smoothing Techniques for Nonlinear Programming

José Luis Romero (), Damián Fernández () and Germán Ariel Torres ()
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José Luis Romero: Universidad Nacional de Córdoba (CIEM–CONICET)
Damián Fernández: Universidad Nacional de Córdoba (CIEM–CONICET)
Germán Ariel Torres: Universidad Nacional del Nordeste (IMIT–CONICET)

Journal of Optimization Theory and Applications, 2026, vol. 208, issue 1, No 23, 43 pages

Abstract: Abstract This paper proposes a novel approach to solving nonlinear programming problems using a sharp augmented Lagrangian method with a smoothing technique. Traditional sharp augmented Lagrangian methods are known for their effectiveness but are often hindered by the need for global minimization of nonconvex, nondifferentiable functions at each iteration. To address this challenge, we introduce a smoothing function that approximates the sharp augmented Lagrangian, enabling the use of primal minimization strategies similar to those in Powell–Hestenes–Rockafellar (PHR) methods. Our approach retains the theoretical rigor of classical duality schemes while allowing for the use of stationary points in the primal optimization process. We present two algorithms based on this method–one utilizing standard descent and the other employing coordinate descent. Numerical experiments demonstrate that our smoothing–based method compares favorably with the PHR augmented Lagrangian approach, offering both robustness and practical efficiency. The proposed method is particularly advantageous in scenarios where exact minimization is computationally infeasible, providing a balance between theoretical precision and computational tractability.

Keywords: Augmented sharp lagrangian; Continuous optimization; Nonlinear programming; 49J53; 49K99; 90C30 (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/s10957-025-02847-6

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