The focus in this paper is interior-point methods for bound-constrained nonlinear optimization where the system of nonlinear equations that arise are solved with Newton’s method. There is a trade-off between solving Newton systems directly, which give high quality solutions, and solving many approximate Newton systems which are computationally less expensive but give lower quality solutions. We propose partial and full approximate solutions to the Newton systems, which in general involves solving a reduced system of linear equations. The specific approximate solution and the size of the reduced system that needs to be solved at each iteration are determined by estimates of the active and inactive constraints at the solution. These sets are at each iteration estimated by a simple heuristic. In addition, we motivate and suggest two modified-Newton approaches which are based on an intermediate step that consists of the partial approximate solutions. The theoretical setting is introduced and asymptotic error bounds are given along with numerical results for bound-constrained convex quadratic optimization problems, both random and from the CUTEst test collection.