We address the solution of constrained nonlinear systems by new linesearch quasi-Newton methods. These methods are based on a proper use of the projection map onto the convex constraint set and on a derivative-free and nonmonotone linesearch strategy. The convergence properties of the proposed methods are presented along with a worst-case iteration complexity bound. Several implementations of the proposed scheme are discussed and validated on bound-constrained problems including gas distribution network models. The results reported show that the new methods are very efficient and competitive with an existing affine-scaling procedure.

Quasi-Newton methods for constrained nonlinear systems: complexity analysis and applications / Marini L.; Morini B.; Porcelli M.. - In: COMPUTATIONAL OPTIMIZATION AND APPLICATIONS. - ISSN 0926-6003. - STAMPA. - 71:1(2018), pp. 147-170. [10.1007/s10589-018-9980-7]

Quasi-Newton methods for constrained nonlinear systems: complexity analysis and applications

Porcelli M.
2018

Abstract

We address the solution of constrained nonlinear systems by new linesearch quasi-Newton methods. These methods are based on a proper use of the projection map onto the convex constraint set and on a derivative-free and nonmonotone linesearch strategy. The convergence properties of the proposed methods are presented along with a worst-case iteration complexity bound. Several implementations of the proposed scheme are discussed and validated on bound-constrained problems including gas distribution network models. The results reported show that the new methods are very efficient and competitive with an existing affine-scaling procedure.
2018
Quasi-Newton methods for constrained nonlinear systems: complexity analysis and applications / Marini L.; Morini B.; Porcelli M.. - In: COMPUTATIONAL OPTIMIZATION AND APPLICATIONS. - ISSN 0926-6003. - STAMPA. - 71:1(2018), pp. 147-170. [10.1007/s10589-018-9980-7]
Marini L.; Morini B.; Porcelli M.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/711331
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