Abstract
Preconditioned iterative methods provide an effective alternative to direct methods for the solution of the KKT linear systems arising in Interior Point algorithms, especially when large-scale problems are considered. We analyze the behaviour of a Constraint Preconditioner coupled with Krylov solvers, in a Potential Reduction (PR) framework. We present also adaptive stopping criteria for the inner iterations that relate the accuracy in the solution of the KKT system to the quality of the current PR iterate, to increase the overall computational efficiency. Numerical experiments on a set of large-scale problems show the effectiveness of this approach. [ DOI : 10.1685 / CSC06031] About DOI
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