Achieving linear or quadratic convergence on piecewise smooth optimization problems

Andreas Griewank, Andrea Walther

Nov 03, 2017 (modified: Nov 03, 2017) NIPS 2017 Workshop Autodiff 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.