Decentralized Riemannian Gradient Descent on the Stiefel Manifold

We consider distributed non-convex optimization where a network of agents aims at minimizing a global function over the Stiefel manifold. The global function is represented as a finite sum of smooth local functions, where each local function is associated with one agent and agents communicate with each other over an undirected connected graph. The problem is non-convex as local functions are possibly non-convex (but smooth) and the Steifel manifold is a non-convex set. We present a decentralized Riemannian stochastic gradient method (DRSGD) with the convergence rate of O(1/√K) to a stationary point. To have exact convergence with constant stepsize, we also propose a decentralized Riemannian gradient tracking algorithm (DRGTA) with the convergence rate of O(1/K) to a stationary point. We use multi-step consensus to preserve the iteration in the local consensus region. DRGTA is the first decentralized algorithm with exact convergence for distributed optimization on Stiefel manifold.