Explicit Global Convergence Rates of BFGS without Line Search

This paper studies the convergence rates of the Broyden--Fletcher--Goldfarb--Shanno~(BFGS) method without line search. We show that the BFGS method with an adaptive step size [Gao and Goldfarb, Optimization Methods and Software, 34(1):194-217, 2019] exhibits a two-phase non-asymptotic global convergence behavior when minimizing a strongly convex function, i.e., a linear convergence rate of $\mathcal{O}((1 - 1 / \varkappa)^{k})$ in the first phase and a superlinear convergence rate of $\mathcal{O}((\varkappa / k)^{k})$ in the second phase, where $k$ is the iteration counter and $\varkappa$ is the condition number. In contrast, the existing analysis only establishes asymptotic results. Furthermore, we propose a novel adaptive BFGS method without line search, which allows a larger step size by taking the gradient Lipschitz continuity into the algorithm design. We prove that our method achieves faster convergence when the initial point is far away from the optimal solution.