Abstract

In this work we study the parallel coordinate descent method (PCDM) proposed by Richtárik and Takáč [26] for minimizing a regularized convex function. We adopt elements from the work of Lu and Xiao [39], and combine them with several new insights, to obtain sharper iteration complexity results for PCDM than those presented in [26]. Moreover, we show that PCDM is monotonic in expectation, which was not confirmed in [26], and we also derive the first high probability iteration complexity result where the initial levelset is unbounded.