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The function has positive values at points x inside Ω, it decreases in value as x approaches the boundary of Ω where the signed distance function is zero, and it takes negative values outside of Ω. [1] However, the alternative convention is also sometimes taken instead (i.e., negative inside Ω and positive outside). [2]
In the mathematical field of graph theory, the distance between two vertices in a graph is the number of edges in a shortest path (also called a graph geodesic) connecting them. This is also known as the geodesic distance or shortest-path distance. [1] Notice that there may be more than one shortest path between two vertices. [2]
Objects are detected out to a pre-determined maximum detection distance w. Not all objects within w will be detected, but a fundamental assumption is that all objects at zero distance (i.e., on the line itself) are detected. Overall detection probability is thus expected to be 1 on the line, and to decrease with increasing distance from the line.
The enumeration formulas for unit distance graphs generalize to higher dimensions, and shows that in dimensions four or more the number of strict unit distance graphs is much larger than the number of subgraphs of unit distance graphs. [2] Any finite graph may be embedded as a unit distance graph in a sufficiently high dimension.
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.
Draw distance requires definition because a processor having to render objects out to an infinite distance would slow down the application to an unacceptable speed. [1] As the draw distance increases, more distant polygons need to be drawn onto the screen that would regularly be clipped .
The running time of this algorithm when run on a polyline consisting of n – 1 segments and n vertices is given by the recurrence T(n) = T(i + 1) + T(n − i) + O where i = 1, 2,..., n − 2 is the value of index in the pseudocode. In the worst case, i = 1 or i = n − 2 at each recursive invocation yields a running time of O(n 2).
The most widely known string metric is a rudimentary one called the Levenshtein distance (also known as edit distance). [2] It operates between two input strings, returning a number equivalent to the number of substitutions and deletions needed in order to transform one input string into another.