Trust region methods are a class of numerical methods for optimization. Unlike line search
type methods where a line search is carried out in each iteration, trust region methods …

Heritability analyses of genome-wide association study (GWAS) cohorts have yielded
important insights into complex disease architecture, and increasing sample sizes hold the …

Abstract An Adaptive Regularisation algorithm using Cubics (ARC) is proposed for
unconstrained optimization, generalizing at the same time an unpublished method due to …

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 …

A globally convergent optimization algorithm for solving large nonlinear optimal power flow
(OPF) problems is presented. As power systems become heavily loaded, there is an …

The objectives of this paper are twofold. We devise a general framework for identifying
locally optimal algorithmic parameters. Algorithmic parameters are treated as decision …

This paper presents a new systematic method for identifying the values of the machine-tool
settings required to obtain flank form modifications in hypoid gears. The problem is given a …

We consider an efficient trust-region framework which employs a new nonmonotone line
search technique for unconstrained optimization problems. Unlike the traditional …

The monotone trust-region methods are well-known techniques for solving unconstrained
optimization problems. While it is known that the nonmonotone strategies not only can …

Nonlinear systems play a significant role in numerous scientific and engineering disciplines,
and comprehending their behavior is crucial for the development of effective control and …