Do you know the difference between an optimist and a pessimist? The former believes we
live in the best possible world, and the latter is afraid that the former might be right.… In that …

Adaptive cubic regularization methods have emerged as a credible alternative to linesearch
and trust-region for smooth nonconvex optimization, with optimal complexity amongst …

An observable microgrid may become unobservable when sensors are at fault, sensor data
is missing, or data has been tampered by malicious agents. In those cases, state estimation …

In this paper, we propose a stochastic Levenberg–Marquardt algorithm based on trust
region for stochastic nonlinear least squares problems, where the stochastic Jacobians and …

Composite minimization involves a collection of functions which are aggregated in a
nonsmooth manner. It covers, as a particular case, smooth approximation of minimax …

High-order tensor methods that employ Taylor-based local models (of degree $ p\ge 3$)
within adaptive regularization frameworks have been recently proposed for both convex and …

Globally convergent variants of the Gauss--Newton algorithm are often the methods of
choice to tackle nonlinear least-squares problems. Among such frameworks, Levenberg …

We consider the solution of nonlinear least-squares problems. Such problems have
traditionally been solved using a Gauss-Newton or Newton approximation, which is in turn …

Recently, the optimization methods for computing higher-order critical points of nonconvex
problems attract growing research interest (Anandkumar Conference on Learning Theory 81 …

An observable microgrid may become unobservable when sensors are at fault, sensor data
is missing, or data has been tampered by malicious agents. In those cases, state estimation …