Some worst-case datasets of deterministic first-order methods for solving binary logistic regression

Yuyuan Ouyang; Trevor Squires

doi:10.3934/ipi.2020047

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2021, Volume 15, Issue 1: 63-77. Doi: 10.3934/ipi.2020047

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Some worst-case datasets of deterministic first-order methods for solving binary logistic regression

Yuyuan Ouyang^, and
Trevor Squires

School of Mathematical and Statistical Sciences, Clemson University, Clemson, SC 29634, USA

^* Corresponding author: Yuyuan Ouyang
^* Corresponding author: Yuyuan Ouyang

Received: December 2019

Revised: April 2020

Early access: August 2020

Published: February 2021

The authors are supported by National Science Foundation grant DMS-1913006 and Office of Naval Research grant N00014-19-1-2295

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Abstract

We present in this paper some worst-case datasets of deterministic first-order methods for solving large-scale binary logistic regression problems. Under the assumption that the number of algorithm iterations is much smaller than the problem dimension, with our worst-case datasets it requires at least $ {{{\mathcal O}}}(1/\sqrt{\varepsilon}) $ first-order oracle inquiries to compute an $ \varepsilon $-approximate solution. From traditional iteration complexity analysis point of view, the binary logistic regression loss functions with our worst-case datasets are new worst-case function instances among the class of smooth convex optimization problems.

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Mathematics Subject Classification: Primary: 90C06, 90C25, 90C30.

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