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A second derivative trust-region SQP method with a "trust-region-free" predictor step
2009
In (NAR 08/18 and 08/21, Oxford University Computing Laboratory 2008) we introduced a second-derivative SQP method (S2QP) for solving nonlinear nonconvex optimization problems. We proved that the method is globally convergent and locally superlinearly convergent under standard assumptions. A critical component of the algorithm is the so-called predictor step, which is computed from a strictly convex quadratic program with a trust-region constraint. This step is essential for proving global
doi:10.5286/raltr.2009024
fatcat:kwyyrvgibzhv7fdx2px2qze3pa