  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.
