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  • Recently proposed MPRGP algorithm for the solution of strictly convex quadratic programming problems was proved to enjoy R-linear convergence in bounds on the spectrum. In this talk we consider two methods of preconditioning that improved the theoretical rate of convergence including nonlinear steps, namely preconditioning by a conjugate projector and edge averaging for the variational inequalities discretized by FETI-DP domain decomposition. Theoretical results are confirmed by numerical experiments.
  • Recently proposed MPRGP algorithm for the solution of strictly convex quadratic programming problems was proved to enjoy R-linear convergence in bounds on the spectrum. In this talk we consider two methods of preconditioning that improved the theoretical rate of convergence including nonlinear steps, namely preconditioning by a conjugate projector and edge averaging for the variational inequalities discretized by FETI-DP domain decomposition. Theoretical results are confirmed by numerical experiments. (en)
Title
  • Preconditioning for Bound Constrained Quadratic Programming Problems
  • Preconditioning for Bound Constrained Quadratic Programming Problems (en)
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  • Preconditioning for Bound Constrained Quadratic Programming Problems
  • Preconditioning for Bound Constrained Quadratic Programming Problems (en)
skos:notation
  • RIV/61989100:27240/08:00021142!RIV11-AV0-27240___
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  • 388717
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  • RIV/61989100:27240/08:00021142
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  • preconditioning, projector, MPRGP (en)
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  • [E40212350692]
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  • Boston, USA
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  • Dostál, Zdeněk
  • Domorádová, Marta
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  • 27240
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