Search results
Results from the WOW.Com Content Network
Nearest neighbor search (NNS), as a form of proximity search, is the optimization problem of finding the point in a given set that is closest (or most similar) to a given point. Closeness is typically expressed in terms of a dissimilarity function: the less similar the objects, the larger the function values.
The matching pursuit is an example of a greedy algorithm applied on signal approximation. A greedy algorithm finds the optimal solution to Malfatti's problem of finding three disjoint circles within a given triangle that maximize the total area of the circles; it is conjectured that the same greedy algorithm is optimal for any number of circles.
Best-first search is a class of search algorithms which explores a graph by expanding the most promising node chosen according to a specified rule.. Judea Pearl described best-first search as estimating the promise of node n by a "heuristic evaluation function () which, in general, may depend on the description of n, the description of the goal, the information gathered by the search up to ...
The greedy randomized adaptive search procedure (also known as GRASP) is a metaheuristic algorithm commonly applied to combinatorial optimization problems. GRASP typically consists of iterations made up from successive constructions of a greedy randomized solution and subsequent iterative improvements of it through a local search . [ 1 ]
[3] [4] Steven Minton and Andy Philips analyzed the neural network algorithm and separated it into two phases: (1) an initial assignment using a greedy algorithm and (2) a conflict minimization phases (later to be called "min-conflicts"). A paper was written and presented at AAAI-90; Philip Laird provided the mathematical analysis of the algorithm.
A random walk algorithm sometimes moves like a greedy algorithm but sometimes moves randomly. It depends on a parameter p {\displaystyle p} , which is a real number between 0 and 1. At every move, with probability p {\displaystyle p} the algorithm proceeds like a greedy algorithm, trying to maximally decrease the cost of the assignment.
For dense instances, however, there exists a -approximation algorithm for every >. [8] Tight example for the greedy algorithm with k=3. There is a standard example on which the greedy algorithm achieves an approximation ratio of /.
In the undirected case, the greedy tour is at most O(ln n)-times longer than an optimal tour. [1] The best lower bound known for any deterministic online algorithm is 10/3. [2] Unit weight undirected graphs can be explored with a competitive ration of 2 − ε, [3] which is already a tight bound on Tadpole graphs. [4]