Enriched Methods for Large-Scale Unconstrained Optimization

  title={Enriched Methods for Large-Scale Unconstrained Optimization},
  author={Jos{\'e} Luis Morales and Jorge Nocedal},
  journal={Comp. Opt. and Appl.},
This paper describes a class of optimization methods that interlace iterations of the limited memory BFGS method L BFGS and a Hessian free Newton method HFN in such a way that the information collected by one type of iteration improves the performance of the other Curvature information about the objective function is stored in the form of a limited memory matrix and plays the dual role of preconditioning the inner conjugate gradient iteration in the HFN method and of providing a warm start for… CONTINUE READING
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