We derive compact representations of BFGS and symmetric rank-one matri
ces for optimization. These representations allow us to efficiently im
plement limited memory methods for large constrained optimization prob
lems. In particular, we discuss how to compute projections of limited
memory matrices onto subspaces. We also present a compact representati
on of the matrices generated by Broyden's update for solving systems o
f nonlinear equations.