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Generating Random Points (or Vectors) Controlling the Percentage of them that Are Extreme in their Convex (or Positive) Hull

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Journal of Mathematical Modelling and Algorithms

Abstract

This paper presents a technique to generate random data in dimensional space m such that their convex (or positive) hull contains a specific percentage of extreme points (or vectors), determined by the analyst or generator of the data. The methodology strives to remove symmetry, regularity, or predictability, which may be desirable in data used to test or compare algorithms or heuristics. There are numerous applications for this methodology.

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Authors and Affiliations

  1. Department of Information and Decision Sciences, University of Texas at El Paso, 500 W. University, El Paso, TX, 79968-0544, USA

    F. J. López

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  1. F. J. López
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Correspondence to F. J. López.

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Mathematics Subject Classifications (2000)

65C10, 52B11, 52B55.

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López, F.J. Generating Random Points (or Vectors) Controlling the Percentage of them that Are Extreme in their Convex (or Positive) Hull. J Math Model Algor 4, 219–234 (2005). https://doi.org/10.1007/s10852-005-1597-z

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  • DOI: https://doi.org/10.1007/s10852-005-1597-z

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