We will discuss how certain properties of quasi-Newton methods have been exploited to derive an efficient algorithm for unconstrained optimization, which works by restricting search directions to a subspace at each iteration. Then we will present a new algorithm, RH-B, which applies these principles to problems with bound constraints. This will include a discussion about issues with the current implementation, suggestions for future versions and numerical results.