Compressed Gradient Tracking for Decentralized Optimization Over General Directed Networks

arXiv:2106.07243v4 Announce Type: replace-cross
Abstract: In this paper, we propose two communication efficient decentralized optimization algorithms over a general directed multi-agent network. The first algorithm, termed Compressed Push-Pull (CPP), combines the gradient tracking Push-Pull method with communication compression. We show that CPP is applicable to a general class of unbiased compression operators and achieves linear convergence rate for strongly convex and smooth objective functions. The second algorithm is a broadcast-like version of CPP (B-CPP), and it also achieves linear convergence …

abstract agent algorithm algorithms arxiv class communication compression cpp cs.dc cs.lg cs.ma decentralized eess.sp general gradient math.oc multi-agent network networks optimization paper show tracking type unbiased