Nonmonotone spectral gradient (NSG) techniques are considered for unconstrained optimization of differentiable functions. They combine a nonmonotone step length strategy, that is based on the Grippo–Lampariello–Lucidi nonmonotone line search, with the spectral gradient choice of steplength. In this paper, we present a hybrid nonmonotone spectral gradient method. In each iteration after finding an acceptable point by NSG method, we employ an extrapolation step to find a even better point by using particle swarm optimization. Through specific assumptions, we prove the global convergence of our proposed algorithm
Computational and Applied Mathematics – Springer Journals
Published: Jan 25, 2016
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