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In this paper we study the effect of the way that the data is partitioned indistributed optimization. The original DiSCO algorithm [Communication-EfficientDistributed Optimization of Self-Concordant Empirical Loss, Yuchen Zhang andLin Xiao, 2015] partitions the input data based on samples. We describe how theoriginal algorithm has to be modified to allow partitioning on features andshow its efficiency both in theory and also in practice.

Partitioning Data on Features or Samples in Communication-Efficient Distributed Optimization?