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Perturbed Augmented Lagrangian Method Framework with Applications to Proximal and Smoothed Variants

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  • A. F. Izmailov

    (Lomonosov Moscow State University, MSU Uchebniy Korpus 2)

  • M. V. Solodov

    (IMPA – Instituto de Matemática Pura e Aplicada)

Abstract

We introduce a perturbed augmented Lagrangian method framework, which is a convenient tool for local analyses of convergence and rates of convergence of some modifications of the classical augmented Lagrangian algorithm. One example to which our development applies is the proximal augmented Lagrangian method. Previous results for this version required twice differentiability of the problem data, the linear independence constraint qualification, strict complementarity, and second-order sufficiency; or the linear independence constraint qualification and strong second-order sufficiency. We obtain a set of convergence properties under significantly weaker assumptions: once (not twice) differentiability of the problem data, uniqueness of the Lagrange multiplier, and second-order sufficiency (no linear independence constraint qualification and no strict complementarity); or even second-order sufficiency only. Another version to which the general framework applies is the smoothed augmented Lagrangian method, where the plus-function associated with penalization of inequality constraints is approximated by a family of smooth functions (so that the subproblems are twice differentiable if the problem data are). Furthermore, for all the modifications, inexact solution of subproblems is handled naturally. The presented framework also subsumes the basic augmented Lagrangian method, both exact and inexact.

Suggested Citation

  • A. F. Izmailov & M. V. Solodov, 2022. "Perturbed Augmented Lagrangian Method Framework with Applications to Proximal and Smoothed Variants," Journal of Optimization Theory and Applications, Springer, vol. 193(1), pages 491-522, June.
    • Handle: RePEc:spr:joptap:v:193:y:2022:i:1:d:10.1007_s10957-021-01914-y
    DOI: 10.1007/s10957-021-01914-y
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    References listed on IDEAS

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    1. A. F. Izmailov & M. V. Solodov, 2015. "Newton-Type Methods: A Broader View," Journal of Optimization Theory and Applications, Springer, vol. 164(2), pages 577-620, February.
    2. A. Izmailov & A. Kurennoy & M. Solodov, 2015. "Local convergence of the method of multipliers for variational and optimization problems under the noncriticality assumption," Computational Optimization and Applications, Springer, vol. 60(1), pages 111-140, January.
    3. R. T. Rockafellar, 1976. "Augmented Lagrangians and Applications of the Proximal Point Algorithm in Convex Programming," Mathematics of Operations Research, INFORMS, vol. 1(2), pages 97-116, May.
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