A fresh look at nonsmooth Levenberg–Marquardt methods with applications to bilevel optimization
A fresh look at nonsmooth Levenberg–Marquardt methods with applications to bilevel optimization
In this paper, we revisit the classical problem of solving over-determined systems of nonsmooth equations numerically. We suggest a nonsmooth Levenberg–Marquardt method for its solution which, in contrast to the existing literature, does not require local Lipschitzness of the data functions. This is possible when using Newton-differentiability instead of semismoothness as the underlying tool of generalized differentiation. Conditions for local fast convergence of the method are given. Afterwards, in the context of over-determined mixed nonlinear complementarity systems, our findings are applied, and globalized solution methods, based on a residual induced by the maximum and the Fischer–Burmeister function, respectively, are constructed. The assumptions for local fast convergence are worked out and compared. Finally, these methods are applied for the numerical solution of bilevel optimization problems. We recall the derivation of a stationarity condition taking the shape of an over-determined mixed nonlinear complementarity system involving a penalty parameter, formulate assumptions for local fast convergence of our solution methods explicitly, and present results of numerical experiments. Particularly, we investigate whether the treatment of the appearing penalty parameter as an additional variable is beneficial or not.
Bilevel optimization, mixed nonlinear complementarity problems, Newton-differentiability, nonsmooth Levenberg–Marquardt methods
Jolaoso, Lateef O.
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Mehlitz, Patrick
eecbbf4c-dc3f-44d5-b448-3053f23874f4
Zemkoho, Alain B.
30c79e30-9879-48bd-8d0b-e2fbbc01269e
12 March 2024
Jolaoso, Lateef O.
102467df-eae0-4692-8668-7f73e8e02546
Mehlitz, Patrick
eecbbf4c-dc3f-44d5-b448-3053f23874f4
Zemkoho, Alain B.
30c79e30-9879-48bd-8d0b-e2fbbc01269e
Jolaoso, Lateef O., Mehlitz, Patrick and Zemkoho, Alain B.
(2024)
A fresh look at nonsmooth Levenberg–Marquardt methods with applications to bilevel optimization.
Optimization.
(doi:10.1080/02331934.2024.2313688).
Abstract
In this paper, we revisit the classical problem of solving over-determined systems of nonsmooth equations numerically. We suggest a nonsmooth Levenberg–Marquardt method for its solution which, in contrast to the existing literature, does not require local Lipschitzness of the data functions. This is possible when using Newton-differentiability instead of semismoothness as the underlying tool of generalized differentiation. Conditions for local fast convergence of the method are given. Afterwards, in the context of over-determined mixed nonlinear complementarity systems, our findings are applied, and globalized solution methods, based on a residual induced by the maximum and the Fischer–Burmeister function, respectively, are constructed. The assumptions for local fast convergence are worked out and compared. Finally, these methods are applied for the numerical solution of bilevel optimization problems. We recall the derivation of a stationarity condition taking the shape of an over-determined mixed nonlinear complementarity system involving a penalty parameter, formulate assumptions for local fast convergence of our solution methods explicitly, and present results of numerical experiments. Particularly, we investigate whether the treatment of the appearing penalty parameter as an additional variable is beneficial or not.
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A fresh look at nonsmooth Levenberg Marquardt methods with applications to bilevel optimization
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Accepted/In Press date: 29 January 2024
e-pub ahead of print date: 12 March 2024
Published date: 12 March 2024
Keywords:
Bilevel optimization, mixed nonlinear complementarity problems, Newton-differentiability, nonsmooth Levenberg–Marquardt methods
Identifiers
Local EPrints ID: 488935
URI: http://eprints.soton.ac.uk/id/eprint/488935
ISSN: 0233-1934
PURE UUID: 23045554-e224-4480-973f-20485c33030a
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Date deposited: 09 Apr 2024 17:04
Last modified: 14 May 2024 02:01
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Author:
Patrick Mehlitz
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