Some Worst-Case Datasets of Deterministic First-Order Methods for Solving Binary Logistic Regression

Yuyuan Ouyang; Trevor Squires

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arXiv · 1908.04091

Some Worst-Case Datasets of Deterministic First-Order Methods for Solving Binary Logistic Regression

Yuyuan Ouyang·Trevor Squires

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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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BibTeX RIS

Yuyuan Ouyang, Trevor Squires. 2019-08-12. Some Worst-Case Datasets of Deterministic First-Order Methods for Solving Binary Logistic Regression. https://arxiv.org/abs/1908.04091

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