We present the Residual Quadratic Programming Active-Set Subspace (ResQPASS) method that solves large-scale linear least-squares problems with bound constraints on the variables. The problem is solved by creating a series of small problems of increasing size by projecting on the basis of residuals. Each projected problem is solved by the active-set method for convex quadratic programming, warm-started with a working set and solution of the previous problem. The method coincides with conjugate gradients (CG) or, equivalently, LSQR applied to the normal equations when none of the constraints is active. When only a few constraints are active the method converges, after a few initial iterations, like the CG and LSQR. An analysis links the convergence to an asymptotic Krylov subspace. We also present an efficient implementation where QR factorizations of the projected are updated over the inner iterations and Cholesky over the outer iterations.

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