  This paper deals with subsampled spectral gradient methods for minimizing
finite sum. Subsample function and gradient approximations are employed in
order to reduce the overall computational cost of the classical spectral
gradient methods. The global convergence is enforced by a nonmonotone line
search procedure. Global convergence is proved when functions and gradients are
approximated with increasing accuracy. R-linear convergence and worst-case
iteration complexity is investigated in case of strongly convex objective
function. Numerical results on well known binary classification problems are
given to show the effectiveness of this framework and analyze the effect of
different spectral coefficient approximations arising from variable sample
nature of this procedure.
