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# On the use of the energy norm in trust-region and adaptive cubic regularization subproblems

3 IRIT-APO - Algorithmes Parallèles et Optimisation
IRIT - Institut de recherche en informatique de Toulouse
Abstract : We consider solving unconstrained optimization problems by means of two popular globalization techniques: trust-region (TR) algorithms and adaptive regularized framework using cubics (ARC). Both techniques require the solution of a so-called subproblem'' in which a trial step is computed by solving an optimization problem involving an approximation of the objective function, called the model". The latter is supposed to be adequate in a neighborhood of the current iterate. In this paper, we address an important practical question related with the choice of the norm for defining the neighborhood. More precisely, assuming here that the Hessian $B$ of the model is symmetric positive definite, we propose the use of the so-called energy norm'' -- defined by $\|x\|_B= \sqrt{x^TBx}$ for all $x \in \real^n$ -- in both TR and ARC techniques. We show that the use of this norm induces remarkable relations between the trial step of both methods that can be used to obtain efficient practical algorithms. We furthermore consider the use of truncated Krylov subspace methods to obtain an approximate trial step for large scale optimization. Within the energy norm, we obtain line search algorithms along the Newton direction, with a special backtracking strategy and an acceptability condition in the spirit of TR/ARC methods. The new line search algorithm, derived by ARC, enjoys a worst-case iteration complexity of $\mathcal{O}(\epsilon^{-3/2})$. We show the good potential of the energy norm on a set of numerical experiments.
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https://hal.archives-ouvertes.fr/hal-01654736
Déposant : Open Archive Toulouse Archive Ouverte (oatao) <>
Soumis le : lundi 4 décembre 2017 - 13:06:11
Dernière modification le : lundi 31 août 2020 - 16:36:38

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El Houcine Bergou, Youssef Diouane, Serge Gratton. On the use of the energy norm in trust-region and adaptive cubic regularization subproblems. Computational Optimization and Applications, Springer Verlag, 2017, vol. 68 (n° 3), pp. 533-554. ⟨10.1007/s10589-017-9929-2⟩. ⟨hal-01654736⟩

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