Gradient Methods for Problems with Inexact Model of the Objective

We consider optimization methods for convex minimization problems under inexact information on the objective function. We introduce inexact model of the objective, which as a particular cases includes \((\delta,L)\) inexact oracle and relative smoothness condition. We analyze gradient method which u...

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Veröffentlicht in:	arXiv.org 2019-03
Hauptverfasser:	Stonyakin, Fedor, Dvinskikh, Darina, Dvurechensky, Pavel, Kroshnin, Alexey, Kuznetsova, Olesya, Agafonov, Artem, Gasnikov, Alexander, Tyurin, Alexander, Uribe, César A, Pasechnyuk, Dmitry, Artamonov, Sergei
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Approximation Center of gravity Clustering Iterative methods Mathematical models Optimization Smoothness
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