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  2. The Erdős Distance Problem - Wikipedia

    en.wikipedia.org/wiki/The_Erdős_Distance_Problem

    The Erdős Distance Problem consists of twelve chapters and three appendices. [5]After an introductory chapter describing the formulation of the problem by Paul Erdős and Erdős's proof that the number of distances is always at least proportional to , the next six chapters cover the two-dimensional version of the problem.

  3. Erdős distinct distances problem - Wikipedia

    en.wikipedia.org/wiki/Erdős_distinct_distances...

    In discrete geometry, the Erdős distinct distances problem states that every set of points in the plane has a nearly-linear number of distinct distances. It was posed by Paul Erdős in 1946 [ 1 ] [ 2 ] and almost proven by Larry Guth and Nets Katz in 2015.

  4. Earth mover's distance - Wikipedia

    en.wikipedia.org/wiki/Earth_mover's_distance

    In computer science, the earth mover's distance (EMD) [1] is a measure of dissimilarity between two frequency distributions, densities, or measures, over a metric space D.

  5. Distance geometry - Wikipedia

    en.wikipedia.org/wiki/Distance_geometry

    Distance geometry is the branch of mathematics concerned with characterizing and studying sets of points based only on given values of the distances between pairs of points. [ 1 ] [ 2 ] [ 3 ] More abstractly, it is the study of semimetric spaces and the isometric transformations between them.

  6. Distance set - Wikipedia

    en.wikipedia.org/wiki/Distance_set

    A 2-distance set is a set of points for which the set of distinct mutual distances has cardinality exactly 2. An example of a 2-distance set is the set of vertices of the regular octahedron. There are various results about 2-distance sets, including a classification of all 2-distance sets in dimension 4. [8]

  7. Friction of distance - Wikipedia

    en.wikipedia.org/wiki/Friction_of_distance

    Other problems that apply the friction of distance are much more difficult (i.e., NP-hard), such as the traveling salesman problem and cluster analysis, and automated tools to solve them (usually using heuristic algorithms such as k-means clustering) are less widely available, or only recently available, in GIS software.

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