Math. Program., Ser. A https://doi.org/10.1007/s10107-018-1293-1 FULL LENGTH PAPER Oracle complexity of second-order methods for smooth convex optimization 1 1 1 Yossi Arjevani · Ohad Shamir · Ron Shiff Received: 12 September 2017 / Accepted: 7 May 2018 © Springer-Verlag GmbH Germany, part of Springer Nature and Mathematical Optimization Society 2018 Abstract Second-order methods, which utilize gradients as well as Hessians to opti- mize a given function, are of major importance in mathematical optimization. In this work, we prove tight bounds on the oracle complexity of such methods for smooth convex functions, or equivalently, the worst-case number of iterations required to optimize such functions to a given accuracy. In particular, these bounds indicate when such methods can or cannot improve on gradient-based methods, whose oracle com- plexity is much better understood. We also provide generalizations of our results to higher-order methods. Keywords Smooth convex optimization · Oracle complexity Mathematics Subject Classiﬁcation 90C25 · 65K05 · 49M37 1 Introduction We consider an unconstrained optimization problem of the form min f (w), (1) w∈R Ron Shiff email@example.com Yossi Arjevani firstname.lastname@example.org Ohad Shamir email@example.com Department of Computer Science, Weizmann Institute of Science, Rehovot, Israel 123 Y. Arjevani et al. where f is a
Mathematical Programming – Springer Journals
Published: May 28, 2018
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