Analysis of the gradient method with an Armijo–Wolfe line search on a class of non-smooth convex functions

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It has long been known that the gradient (steepest descent) method may fail on non-smooth problems, but the examples that have appeared in the literature are either devised specifically to defeat a gradient or subgradient method with an exact line search or are unstable with respect to perturbation of the initial point. We give an analysis of the gradient method with steplengths satisfying the Armijo and Wolfe inexact line search conditions on the non-smooth convex function f (x) = a|x(1)| + ∑n i=2 x(i). We show that if a is sufficiently large, satisfying a condition that depends only on the Armijo parameter, then, when the method is initiated at any point x0 ϵ Rn with (x(1) 0 ≠ 0), the iterates converge to a point (Formula presented.) with (Formula presented.), although f is unbounded below. We also give conditions under which the iterates f (xk)→−∞,), using a specific Armijo–Wolfe bracketing line search. Our experimental results demonstrate that our analysis is reasonably tight.

Original languageEnglish (US)
JournalOptimization Methods and Software
StateAccepted/In press - Jan 1 2019



  • convex optimization
  • non-smooth optimization
  • Steepest descent method

ASJC Scopus subject areas

  • Software
  • Control and Optimization
  • Applied Mathematics

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