Achieving linear or quadratic convergence on piecewise smooth optimization problems

Nov 03, 2017 Submission readers: everyone
  • Abstract: Many problems in machine learning involve objective functions that are piecewise smooth due to the occurrence of absolute values mins and maxes in their evaluation procedures. For such function we derived first order (KKT) and second order (SSC) optimality conditions, which can be checked on the basis of a local piecewise linearization that can be computed in an AD like fashion, e.g. using ADOL-C or Tapenade.
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