  Sequential Residual Methods try to solve nonlinear systems of equations
$F(x)=0$ by iteratively updating the current approximate solution along a
residual-related direction. Therefore, memory requirements are minimal and,
consequently, these methods are attractive for solving large-scale nonlinear
systems. However, the convergence of these algorithms may be slow in critical
cases; therefore, acceleration procedures are welcome. Anderson-like
accelerations are widely used in electronic structure calculations to improve a
fixed point algorithm for finding Self Consistent Field (SCF) solutions of
Hartree-Fock models. In this paper, it is showed how to apply this type of
acceleration to Sequential Residual Methods. The performance of the resulting
algorithm is illustrated by applying it to the solution of very large problems
coming from the discretization of partial differential equations.
