14 citations · 18 across the 5 of their papers we have counts for
5 papers
A Newton-MR algorithm with complexity guarantees for nonconvex smooth unconstrained optimization
Yang Liu, Fred Roosta
In this paper, we consider variants of Newton-MR algorithm for solving unconstrained, smooth, but non-convex optimization problems. Unlike the overwhelming majority of Newton-type…
MINRES: From Negative Curvature Detection to Monotonicity Properties
Yang Liu, Fred Roosta
The conjugate gradient method (CG) has long been the workhorse for inner-iterations of second-order algorithms for large-scale nonconvex optimization. Prominent examples include li…
Descent Properties of an Anderson Accelerated Gradient Method With Restarting
Wenqing Ouyang, Yang Liu, Andre Milzarek
Anderson Acceleration (AA) is a popular acceleration technique to enhance the convergence of fixed-point iterations. The analysis of AA approaches typically focuses on the converge…
Convergence of Newton-MR under Inexact Hessian Information
Yang Liu, Fred Roosta
Recently, there has been a surge of interest in designing variants of the classical Newton-CG in which the Hessian of a (strongly) convex function is replaced by suitable approxima…
Newton-MR: Inexact Newton Method With Minimum Residual Sub-problem Solver
Fred Roosta, Yang Liu, Peng Xu +1
We consider a variant of inexact Newton Method, called Newton-MR, in which the least-squares sub-problems are solved approximately using Minimum Residual method. By construction, N…