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  2. Taxicab geometry - Wikipedia

    en.wikipedia.org/wiki/Taxicab_geometry

    Taxicab geometry or Manhattan geometry is geometry where the familiar Euclidean distance is ignored, and the distance between two points is instead defined to be the sum of the absolute differences of their respective Cartesian coordinates, a distance function (or metric) called the taxicab distance, Manhattan distance, or city block distance.

  3. Chebyshev distance - Wikipedia

    en.wikipedia.org/wiki/Chebyshev_distance

    A sphere formed using the Chebyshev distance as a metric is a cube with each face perpendicular to one of the coordinate axes, but a sphere formed using Manhattan distance is an octahedron: these are dual polyhedra, but among cubes, only the square (and 1-dimensional line segment) are self-dual polytopes.

  4. Similarity measure - Wikipedia

    en.wikipedia.org/wiki/Similarity_measure

    Manhattan distance is commonly used in GPS applications, as it can be used to find the shortest route between two addresses. [ citation needed ] When you generalize the Euclidean distance formula and Manhattan distance formula you are left with the Minkowski distance formulas, which can be used in a wide variety of applications.

  5. Minkowski distance - Wikipedia

    en.wikipedia.org/wiki/Minkowski_distance

    The Minkowski distance or Minkowski metric is a metric in a normed vector space which can be considered as a generalization of both the Euclidean distance and the Manhattan distance. It is named after the Polish mathematician Hermann Minkowski .

  6. Multidimensional scaling - Wikipedia

    en.wikipedia.org/wiki/Multidimensional_scaling

    Multidimensional scaling (MDS) is a means of visualizing the level of similarity of individual cases of a data set. MDS is used to translate distances between each pair of objects in a set into a configuration of points mapped into an abstract Cartesian space.

  7. Sum of absolute differences - Wikipedia

    en.wikipedia.org/wiki/Sum_of_absolute_differences

    These differences are summed to create a simple metric of block similarity, the L 1 norm of the difference image or Manhattan distance between two image blocks. The sum of absolute differences may be used for a variety of purposes, such as object recognition , the generation of disparity maps for stereo images, and motion estimation for video ...

  8. Euclidean distance - Wikipedia

    en.wikipedia.org/wiki/Euclidean_distance

    The distance between any two points on the real line is the absolute value of the numerical difference of their coordinates, their absolute difference.Thus if and are two points on the real line, then the distance between them is given by: [1]

  9. Travelling salesman problem - Wikipedia

    en.wikipedia.org/wiki/Travelling_salesman_problem

    The case where the distance from A to B is not equal to the distance from B to A is called asymmetric TSP. A practical application of an asymmetric TSP is route optimization using street-level routing (which is made asymmetric by one-way streets, slip-roads, motorways, etc.). The stacker crane problem can be viewed as a special case of the ...