A new technique for inconsistent quadratic programming problems in the sequential quadratic programming method

Successful treatment of inconsistent quadratic programming (QP) problems is of major importance in the sequential quadratic programming method, since such occur quite often even for well behaved nonlinear programming problems. This paper presents a new technique for regularizing inconsistent QP problems, which compromises in its properties between the simple technique of Pantoja and Mayne and the highly successful, but expensive one of Tone. Global convergence of a corresponding algorithm is shown under reasonable weak conditions. Numerical results are reported which show that this technique, combined with a special method for the case of regular subproblems, is quite competitive to highly appreciated established ones.