  In Part I of this work, we have proposed a general framework of decentralized
stochastic quasi-Newton methods, which converge linearly to the optimal
solution under the assumption that the local Hessian inverse approximations
have bounded positive eigenvalues. In Part II, we specify two fully
decentralized stochastic quasi-Newton methods, damped regularized
limited-memory DFP (Davidon-Fletcher-Powell) and damped limited-memory BFGS
(Broyden-Fletcher-Goldfarb-Shanno), to locally construct such Hessian inverse
approximations without extra sampling or communication. Both of the methods use
a fixed moving window of $M$ past local gradient approximations and local
decision variables to adaptively construct positive definite Hessian inverse
approximations with bounded eigenvalues, satisfying the assumption in Part I
for the linear convergence. For the proposed damped regularized limited-memory
DFP, a regularization term is added to improve the performance. For the
proposed damped limited-memory BFGS, a two-loop recursion is applied, leading
to low storage and computation complexity. Numerical experiments demonstrate
that the proposed quasi-Newton methods are much faster than the existing
decentralized stochastic first-order algorithms.
