Network Newton – Part II : Convergence Rate and Implementation

  title={Network Newton – Part II : Convergence Rate and Implementation},
  author={Aryan Mokhtari and Qing Ling and Alejandro R. Ribeiro},
The use of network Newton methods for the decentralized optimization of a sum cost distributed through agents of a network is considered. Network Newton methods reinterpret distributed gradient descent as a penalty method, observe that the corresponding Hessian is sparse, and approximate the Newton step by truncating a Taylor expansion of the inverse Hessian. Truncating the series at K terms yields the NN-K that requires aggregating information from K hops away. Network Newton is introduced and… CONTINUE READING
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