2020/03/24 by Thinh T. Doan, Doan, Thinh T., Lam M. Nguyen +5 · 3 citations
Computer Science · Engineering · Mathematics · #Complexity and Algorithms in Graphs #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Optimization and Control (math.OC) #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques #cs.LG #math.OC
paper · pdf · doi:10.48550/arxiv.2003.10973
openalex publication_date 2020/03/24 · arxiv created 2020/04/01 · arxiv updated 2020/04/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Motivated by broad applications in reinforcement learning and machine learning, this paper considers the popular stochastic gradient descent (SGD) when the gradients of the underlying objective function are sampled from Markov processes. This Markov sampling leads to the gradient samples being biased and not independent. The existing results for the convergence of SGD under Markov randomness are often established under the assumptions on the boundedness of either the iterates or the gradient samples. Our main focus is to study the finite-time convergence of SGD for different types of objective functions, without requiring these assumptions. We show that SGD converges nearly at the same rate with Markovian gradient samples as with independent gradient samples. The only difference is a logarithmic factor that accounts for the mixing time of the Markov chain.