Newton's method for constrained norm minimization and its application to weighted graph problems

Abstract : Due to increasing computer processing power, Newton's method is receiving again increasing interest for solving optimization problems. In this paper, we provide a methodology for solving smooth norm optimization problems under some linear constraints using the Newton's method. This problem arises in many machine learning and graph optimization applications. We consider as a case study optimal weight selection for average consensus protocols for which we show how Newton's method significantly outperforms gradient methods both in terms of convergence speed and in term of robustness to the step size selection.
Type de document :
Communication dans un congrès
American Control Conference (ACC 2014), Jun 2014, Portland, United States. pp.2983-2988, 〈http://acc2014.a2c2.org/〉. 〈10.1109/ACC.2014.6858611〉
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https://hal.inria.fr/hal-01087706
Contributeur : Mahmoud El Chamie <>
Soumis le : mercredi 26 novembre 2014 - 15:11:39
Dernière modification le : samedi 27 janvier 2018 - 01:31:41

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Mahmoud El Chamie, Giovanni Neglia. Newton's method for constrained norm minimization and its application to weighted graph problems. American Control Conference (ACC 2014), Jun 2014, Portland, United States. pp.2983-2988, 〈http://acc2014.a2c2.org/〉. 〈10.1109/ACC.2014.6858611〉. 〈hal-01087706〉

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