# Polynomial time multiplicative approximation algorithms for logistic regression?

Typically algorithms for logistic regression have an iterative aspect since the problem does not admit a closed form solution. By extension, most iterative algorithms (gradient descent etc.) for logistic regression only admit additive error bounds to the loss function.

My question: does logistic regression admit any finite-factor multiplicative approximation algorithm with polynomial dependence on the dimension $$d$$ and the number of points $$n$$?