We describe an algorithm for solving nonlinear optimization problems with lower and upper bounds that constrain the variables. The algorithm uses projected gradients to construct a limited memory BFGS matrix and determine a step direction. We will compare and contrast it to a similar algortihm L-BFGS-B, and show numerical results demonstrate is effectiveness on a set of large test problems