no code implementations • 16 Feb 2023 • Ruichen Jiang, Qiujiang Jin, Aryan Mokhtari
Quasi-Newton algorithms are among the most popular iterative methods for solving unconstrained minimization problems, largely due to their favorable superlinear convergence property.
no code implementations • NeurIPS 2021 • Qiujiang Jin, Aryan Mokhtari
In this paper, we use an adaptive sample size scheme that exploits the superlinear convergence of quasi-Newton methods globally and throughout the entire learning process.
no code implementations • 30 Mar 2020 • Qiujiang Jin, Aryan Mokhtari
In this paper, we provide a finite-time (non-asymptotic) convergence analysis for Broyden quasi-Newton algorithms under the assumptions that the objective function is strongly convex, its gradient is Lipschitz continuous, and its Hessian is Lipschitz continuous at the optimal solution.