Kamil A. Khan ; Yingwei Yuan - Constructing a subgradient from directional derivatives for functions of two variables

jnsao:6061 - Journal of Nonsmooth Analysis and Optimization, June 12, 2020, Volume 1 - https://doi.org/10.46298/jnsao-2020-6061
Constructing a subgradient from directional derivatives for functions of two variablesArticle

Authors: Kamil A. Khan ORCID; Yingwei Yuan

    For any scalar-valued bivariate function that is locally Lipschitz continuous and directionally differentiable, it is shown that a subgradient may always be constructed from the function's directional derivatives in the four compass directions, arranged in a so-called "compass difference". When the original function is nonconvex, the obtained subgradient is an element of Clarke's generalized gradient, but the result appears to be novel even for convex functions. The function is not required to be represented in any particular form, and no further assumptions are required, though the result is strengthened when the function is additionally L-smooth in the sense of Nesterov. For certain optimal-value functions and certain parametric solutions of differential equation systems, these new results appear to provide the only known way to compute a subgradient. These results also imply that centered finite differences will converge to a subgradient for bivariate nonsmooth functions. As a dual result, we find that any compact convex set in two dimensions contains the midpoint of its interval hull. Examples are included for illustration, and it is demonstrated that these results do not extend directly to functions of more than two variables or sets in higher dimensions.


    Volume: Volume 1
    Section: Original research articles
    Published on: June 12, 2020
    Accepted on: June 4, 2020
    Submitted on: January 30, 2020
    Keywords: Mathematics - Optimization and Control,Mathematics - Numerical Analysis,49J52
    Funding:
      Source : OpenAIRE Graph
    • Funder: Natural Sciences and Engineering Research Council of Canada

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