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A second-derivative trust-region SQP method with a "trust-region-free" predictor step

Abstract:

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 c...

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Publisher:
IMA Journal of Numerical Analysis
Publication date:
2009-11-01
UUID:
uuid:d07d820e-2888-4f1c-822e-0b4cd294f45d
Local pid:
oai:eprints.maths.ox.ac.uk:864
Deposit date:
2011-05-20

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