Nonsmooth Optimization

  • Jonathan M. Borwein
  • Adrian S. Lewis
Part of the CMS Books in Mathematics / Ouvrages de mathématiques de la SMC book series (CMSBM)


From the perspective of optimization, the subdifferential ∂f(·) of a convex function f has many of the useful properties of the derivative. Some examples: it gives the necessary optimality condition 0 ∈ ∂f(x) when the point x is a (local) minimizer (Proposition 3.1.5); it reduces to {∇f(x)} when f is differentiable at x (Corollary 3.1.10); and it often satisfies certain calculus rules such as ∂(f + g)(x) = ∂ f(x) + ∂ g(x) (Theorem 3.3.5). For a variety of reasons, if the function f is not convex, the subdifferential ∂f(·) is not a particularly helpful idea. This makes it very tempting to look for definitions of the subdifferential for a nonconvex function. In this section we outline some examples; the most appropriate choice often depends on context.


Directional Derivative Tangent Cone Nonsmooth Optimization Contingent Cone Clarke Subdifferential 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer Science+Business Media New York 2000

Authors and Affiliations

  • Jonathan M. Borwein
    • 1
  • Adrian S. Lewis
    • 2
  1. 1.Centre for Experimental and Constructive Mathematics, Department of Mathematics and StatisticsSimon Fraser UniversityBurnabyCanada
  2. 2.Department of Combinatorics and OptimizationUniversity of WaterlooWaterlooCanada

Personalised recommendations