Parallel Variable Distribution for Constrained Optimization

@article{Sagastizbal2002ParallelVD,
  title={Parallel Variable Distribution for Constrained Optimization},
  author={Claudia A. Sagastiz{\'a}bal and Mikhail V. Solodov},
  journal={Comp. Opt. and Appl.},
  year={2002},
  volume={22},
  pages={111-131}
}
In the parallel variable distribution framework for solving optimization problems (PVD), the variables are distributed among parallel processors with each processor having the primary responsibility for updating its block of variables while allowing the remaining “secondary” variables to change in a restricted fashion along some easily computable directions. For constrained nonlinear programs convergence theory for PVD algorithms was previously available only for the case of convex feasible set… CONTINUE READING
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Parallel gradient distribution in unconstrained optimization,

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