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  2. Path-based strong component algorithm - Wikipedia

    en.wikipedia.org/wiki/Path-based_strong...

    If the preorder number of w has not yet been assigned (the edge is a tree edge), recursively search w; Otherwise, if w has not yet been assigned to a strongly connected component (the edge is a forward/back/cross edge): Repeatedly pop vertices from P until the top element of P has a preorder number less than or equal to the preorder number of w.

  3. Steinhaus–Johnson–Trotter algorithm - Wikipedia

    en.wikipedia.org/wiki/Steinhaus–Johnson...

    The sequence of permutations generated by the Steinhaus–Johnson–Trotter algorithm has a natural recursive structure, that can be generated by a recursive algorithm. . However the actual Steinhaus–Johnson–Trotter algorithm does not use recursion, instead computing the same sequence of permutations by a simple iterative me

  4. GF (2) - Wikipedia

    en.wikipedia.org/wiki/GF(2)

    every element x of GF(2) satisfies x 2 = x (i.e. is idempotent with respect to multiplication); this is an instance of Fermat's little theorem. GF(2) is the only field with this property (Proof: if x 2 = x, then either x = 0 or x ≠ 0. In the latter case, x must have a multiplicative inverse, in which case dividing both sides by x gives x = 1 ...

  5. SymPy - Wikipedia

    en.wikipedia.org/wiki/SymPy

    SymPy is an open-source Python library for symbolic computation.It provides computer algebra capabilities either as a standalone application, as a library to other applications, or live on the web as SymPy Live [2] or SymPy Gamma. [3]

  6. Parameterized complexity - Wikipedia

    en.wikipedia.org/wiki/Parameterized_complexity

    The W hierarchy is a collection of computational complexity classes. A parameterized problem is in the class W[i], if every instance (,) can be transformed (in fpt-time) to a combinatorial circuit that has weft at most i, such that (,) if and only if there is a satisfying assignment to the inputs that assigns 1 to exactly k inputs.

  7. Hyperparameter optimization - Wikipedia

    en.wikipedia.org/wiki/Hyperparameter_optimization

    In machine learning, hyperparameter optimization [1] or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process, which must be configured before the process starts.

  8. Kabsch algorithm - Wikipedia

    en.wikipedia.org/wiki/Kabsch_algorithm

    Let P and Q be two sets, each containing N points in .We want to find the transformation from Q to P.For simplicity, we will consider the three-dimensional case (=).The sets P and Q can each be represented by N × 3 matrices with the first row containing the coordinates of the first point, the second row containing the coordinates of the second point, and so on, as shown in this matrix:

  9. Streaming algorithm - Wikipedia

    en.wikipedia.org/wiki/Streaming_algorithm

    A special case is the majority problem, which is to determine whether or not any value constitutes a majority of the stream. More formally, fix some positive constant c > 1, let the length of the stream be m, and let f i denote the frequency of value i in the stream. The frequent elements problem is to output the set { i | f i > m/c }. [13]