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In computer science, jump point search (JPS) is an optimization to the A* search algorithm for uniform-cost grids. It reduces symmetries in the search procedure by means of graph pruning, [1] eliminating certain nodes in the grid based on assumptions that can be made about the current node's neighbors, as long as certain conditions relating to the grid are satisfied.
The advantage is that all optimizations of grid A* like jump point search will apply. A visibility graph with all the grid points can be searched with A* for the optimal solution in 2D space. However, the performance is problematic since the number of edges in a graph with V {\displaystyle V} vertices is O ( V 2 ) {\displaystyle O(V^{2})} .
ability to draw the 1st and 2nd derivative and the integral of a plot function; support user-defined constants and parameter values; various tools for plot functions: find minimum/maximum point, get y-value and draw the area between the function and the y-axis
Force-directed graph drawing algorithms assign forces among the set of edges and the set of nodes of a graph drawing.Typically, spring-like attractive forces based on Hooke's law are used to attract pairs of endpoints of the graph's edges towards each other, while simultaneously repulsive forces like those of electrically charged particles based on Coulomb's law are used to separate all pairs ...
The grid file contains the coordinates of the solution grid, while the solution file contains information typical of a CFD solution, flow density, flow momentum (a vector), and flow energy. [2] Data may be stored in either binary or ASCII text format and floating point values may be either single or double precision.
Pathfinding or pathing is the search, by a computer application, for the shortest route between two points. It is a more practical variant on solving mazes . This field of research is based heavily on Dijkstra's algorithm for finding the shortest path on a weighted graph .
Trilinear interpolation is a method of multivariate interpolation on a 3-dimensional regular grid. It approximates the value of a function at an intermediate point (,,) within the local axial rectangular prism linearly, using function data on the lattice points.
The TIN model was developed in the early 1970s as a simple way to build a surface from a set of irregularly spaced points. The first triangulated irregular network program for GIS was written by W. Randolph Franklin, under the direction of David Douglas and Thomas Peucker (Poiker), at Canada's Simon Fraser University, in 1973. [2]