On the convergence rates of generalized conditional gradient method for fully discretized mean field games

We study convergence rates of the generalized conditional gradient (GCG) method applied to fully discretized Mean Field Games (MFG) systems. While explicit convergence rates of the GCG method have been established at the continuous PDE level, a rigorous analysis that simultaneously accounts for time–space discretization and iteration errors has been missing. In this work, we consider an MFG system with a quadratic Hamiltonian including an advection term. We employ the GCG method to construct an iterative algorithm in which the HJB and FP equations are solved separately. At each iteration, we apply the Cole--Hopf transformation to linearize these equations and discretize the resulting linear PDEs using a finite difference method. We then analyze the resulting fully discrete GCG scheme. Under suitable structural assumptions on coupling terms, we establish discrete maximum principles and derive explicit error estimates that quantify both discretization errors and iteration errors within a unified framework. Our estimates show how the convergence rates depend on the mesh sizes and the iteration number, and they reveal a non-uniform behavior with respect to the iteration. Moreover, we prove that higher convergence rates can be achieved under additional regularity assumptions on the solution. Numerical experiments are presented to illustrate the theoretical results and to confirm the predicted convergence behavior.