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Note--A Note on Least Squares Fitting of Functions Constrained to be Either Nonnegative, Nondecreasing or Convex

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  • Warren Dent

    (University of Iowa)

Abstract

Hudson [Hudson, D. J. Least-squares fitting of a polynomial constrained to be either non-negative, non-decreasing or convex. J. R. Statist. Soc. B. 31 113-118.] has described a complicated algorithm for least-squares fitting of polynomials constrained to be either nonnegative, nondecreasing, or convex. Alternate quadratic programming formulations which approximate general functions (not necessarily polynomials) by polygonal segmentation are presented here. The technique is simpler, more general and of wider applicability than that proposed of Hudson.

Suggested Citation

  • Warren Dent, 1973. "Note--A Note on Least Squares Fitting of Functions Constrained to be Either Nonnegative, Nondecreasing or Convex," Management Science, INFORMS, vol. 20(1), pages 130-132, September.
    • Handle: RePEc:inm:ormnsc:v:20:y:1973:i:1:p:130-132
    DOI: 10.1287/mnsc.20.1.130
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    Cited by:

    1. Kuosmanen, Timo & Johnson, Andrew, 2017. "Modeling joint production of multiple outputs in StoNED: Directional distance function approach," European Journal of Operational Research, Elsevier, vol. 262(2), pages 792-801.

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