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 …

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

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 …

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

We present a new theoretical analysis of local superlinear convergence of classical quasi-
Newton methods from the convex Broyden class. As a result, we obtain a significant …

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 present a review of the connections between classical algorithms for solving
Markov Decision Processes (MDPs) and classical gradient-based algorithms in convex …

In this paper, we study and prove the non-asymptotic superlinear convergence rate of the
Broyden class of quasi-Newton algorithms which includes the Davidon–Fletcher–Powell …

We consider minimizing a smooth and strongly convex objective function using a stochastic
Newton method. At each iteration, the algorithm is given an oracle access to a stochastic …

Non-asymptotic analysis of quasi-Newton methods have received a lot of attention recently.
In particular, several works have established a non-asymptotic superlinear rate of …