hyperthyroidism

stock market

conjugate gradient

penalty

outdoor

aerostatic

trust region

quasi-newton

minimax

diabetes_114120

line search

unconstrained optimization

gradient projection

dutch

cave_47853

patronage

amp nbsp

subgradient

ecological vulnerability

nesterov

Optimization methods & software

In this paper, we propose a hybrid conjugate gradient method for unconstrained optimization, obtained by a convex combination of the LS and KMD conjugate gradient parameters. A favourite property of the proposed method is that the search direction satisfies the Dai–Liao conjugacy condition and the quasi-Newton direction. In addition, this property does not depend on the line search. Under a modified strong Wolfe line search, we establish the global convergence of the method. Numerical comparison using a set of 109 unconstrained optimization test problems from the CUTEst library show that the proposed method outperforms the Liu–Storey and Hager–Zhang conjugate gradient methods.

An efficient hybrid conjugate gradient method for unconstrained optimization