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The higher the branching factor, the faster this "explosion" occurs. The branching factor can be cut down by a pruning algorithm. The average branching factor can be quickly calculated as the number of non-root nodes (the size of the tree, minus one; or the number of edges) divided by the number of non-leaf nodes (the number of nodes with ...
In mathematics, particularly computational algebra, Berlekamp's algorithm is a well-known method for factoring polynomials over finite fields (also known as Galois fields). The algorithm consists mainly of matrix reduction and polynomial GCD computations. It was invented by Elwyn Berlekamp in 1967.
There is a polynomial-time algorithm that approximates the minimum Steiner tree to within a factor of +; [11] however, approximating within a factor / is NP-hard. [12] For the restricted case of Steiner Tree problem with distances 1 and 2, a 1.25-approximation algorithm is known. [ 13 ]
An optimal strategy for choosing these polynomials is not known; one simple method is to pick a degree d for a polynomial, consider the expansion of n in base m (allowing digits between −m and m) for a number of different m of order n 1/d, and pick f(x) as the polynomial with the smallest coefficients and g(x) as x − m.
The Cantor–Zassenhaus algorithm takes as input a square-free polynomial (i.e. one with no repeated factors) of degree n with coefficients in a finite field whose irreducible polynomial factors are all of equal degree (algorithms exist for efficiently factoring arbitrary polynomials into a product of polynomials satisfying these conditions, for instance, () / ((), ′ ()) is a squarefree ...
with a corresponding factor graph shown on the right. Observe that the factor graph has a cycle. If we merge (,) (,) into a single factor, the resulting factor graph will be a tree. This is an important distinction, as message passing algorithms are usually exact for trees, but only approximate for graphs with cycles.
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A 100-body simulation with the Barnes–Hut tree visually as blue boxes. The Barnes–Hut simulation (named after Josh Barnes and Piet Hut) is an approximation algorithm for performing an N-body simulation. It is notable for having order O(n log n) compared to a direct-sum algorithm which would be O(n 2). [1]