A new method for solving sequences of quadratic programs (QPs) is presented. For each new QP in the sequence, the method utilizes hot-starts that employ information computed by an active-set QP solver during the solution of the ﬁrst QP. This avoids the computation and factorization of the full matrices for all but the ﬁrst problem in the sequence. The proposed algorithm can be seen as an extension of the iterative reﬁnement procedure for linear systems to QP problems, coupled with the application of an accelerated linear solver method that employs hot-started QP solves as preconditioners. Local convergence results are presented. The practical performance of the proposed method is demonstrated on a sequence of QPs arising in nonlinear model predictive control and during the solution of a set of randomly generated nonlinear optimization problems using sequential quadratic programming. The results show a signiﬁcant reduction in the computation time for large problems with dense constraint matrices, as well as in the number of matrix-vector products.