This paper studies quasi-Newton methods for solving nonlinear equations. We propose
block variants of both good and bad Broyden's methods, which enjoy explicit local …

This paper introduces the Multiple Greedy Quasi-Newton (MGSR1-SP) method, a novel
approach to solving strongly-convex-strongly-concave (SCSC) saddle point problems. Our …

Quasi-Newton algorithms are among the most popular iterative methods for solving
unconstrained minimization problems, largely due to their favorable superlinear …

In this paper, we propose an accelerated quasi-Newton proximal extragradient method for
solving unconstrained smooth convex optimization problems. With access only to the …

This paper studies quasi-Newton methods for strongly-convex-strongly-concave saddle
point problems. We propose random Broyden family updates, which have explicit local …

In this paper, we explore the non-asymptotic global convergence rates of the Broyden-
Fletcher-Goldfarb-Shanno (BFGS) method implemented with exact line search. Notably, due …

We consider the finite-sum optimization problem, where each component function is strongly
convex and has Lipschitz continuous gradient and Hessian. The recently proposed …

This paper proposes a novel class of block quasi-Newton methods for convex optimization
which we call symmetric rank-$ k $(SR-$ k $) methods. Each iteration of SR-$ k …

In this paper, we study the explicit superlinear convergence rates of quasi-Newton methods.
We particularly focus on the classical Broyden's method for solving nonlinear equations. We …

This paper studies the strongly-convex-strongly-concave minimax optimization with
unbalanced dimensionality. Such problems contain several popular applications in data …