  In this paper, we propose a Gradient Descent Bit-Flipping (GDBF) decoding
with momentum, which considers past updates to provide inertia to the decoding
process. We show that GDBF or randomized GDBF decoders with momentum may
closely approach the floating-point Belief-Propagation decoding performance,
and even outperform it in the error-floor region, especially for graphs with
high connectivity degree.
