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Discrete Gradient Methods

In: Introduction to Nonsmooth Optimization

Author

Listed:
  • Adil Bagirov

    (School of Information Technology and Mathematical Sciences, University of Ballarat)

  • Napsu Karmitsa

    (University of Turku)

  • Marko M. Mäkelä

    (University of Turku)

Abstract

In this chapter, we introduce two discrete gradient methods that can be considered as semi-derivative free methods in a sense that they do not use subgradient information and they do not approximate the subgradient but at the end of the solution process (i.e., near the optimal point). The introduced methods are the original discrete gradient method for small-scale nonsmooth optimization and its limited memory bundle version the limited memory discrete gradient bundle method for medium- and semi-large problems.

Suggested Citation

  • Adil Bagirov & Napsu Karmitsa & Marko M. Mäkelä, 2014. "Discrete Gradient Methods," Springer Books, in: Introduction to Nonsmooth Optimization, edition 127, chapter 0, pages 327-333, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-08114-4_15
    DOI: 10.1007/978-3-319-08114-4_15
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