Random Gradient Extrapolation for Distributed and Stochastic Optimization
Random Gradient Extrapolation for Distributed and Stochastic Optimization is a scholarly work, published in 2018 in ''SIAM Journal on Optimization''. The main subjects of the publication include stochastic optimization, optimization problem, mathematics, combinatorics, upper and lower bounds, multi-agent system, mathematical extrapolation, convex function, convex optimization, mathematical optimization, biological function, regular polygon, discrete mathematics, compressed sensing, applied mathematics, machine learning, and logarithm. The authors consider a class of finite-sum convex optimization problems defined over a distributed multiagent network with $m$ agents connected to a central server.