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February  2021, 15(1): 63-77. doi: 10.3934/ipi.2020047

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

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

* Corresponding author: Yuyuan Ouyang

Received  December 2019 Revised  April 2020 Published  August 2020

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

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.

Citation: Yuyuan Ouyang, Trevor Squires. Some worst-case datasets of deterministic first-order methods for solving binary logistic regression. Inverse Problems & Imaging, 2021, 15 (1) : 63-77. doi: 10.3934/ipi.2020047
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