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  2. Column generation - Wikipedia

    en.wikipedia.org/wiki/Column_generation

    Column generation or delayed column generation is an efficient algorithm for solving large linear programs. The overarching idea is that many linear programs are too large to consider all the variables explicitly. The idea is thus to start by solving the considered program with only a subset of its variables.

  3. Pseudorandom number generator - Wikipedia

    en.wikipedia.org/wiki/Pseudorandom_number_generator

    special designs based on mathematical hardness assumptions: examples include the Micali–Schnorr generator, [18] Naor-Reingold pseudorandom function and the Blum Blum Shub algorithm, which provide a strong security proof (such algorithms are rather slow compared to traditional constructions, and impractical for many applications)

  4. List of random number generators - Wikipedia

    en.wikipedia.org/wiki/List_of_random_number...

    Default generator in R and the Python language starting from version 2.3. Xorshift: 2003 G. Marsaglia [26] It is a very fast sub-type of LFSR generators. Marsaglia also suggested as an improvement the xorwow generator, in which the output of a xorshift generator is added with a Weyl sequence.

  5. Linear congruential generator - Wikipedia

    en.wikipedia.org/wiki/Linear_congruential_generator

    The second row is the same generator with a seed of 3, which produces a cycle of length 2. Using a = 4 and c = 1 (bottom row) gives a cycle length of 9 with any seed in [0, 8]. A linear congruential generator (LCG) is an algorithm that yields a sequence of pseudo-randomized numbers calculated with a discontinuous piecewise linear equation.

  6. Middle-square method - Wikipedia

    en.wikipedia.org/wiki/Middle-square_method

    To generate a sequence of n-digit pseudorandom numbers, an n-digit starting value is created and squared, producing a 2n-digit number. If the result has fewer than 2n digits, leading zeroes are added to compensate. The middle n digits of the result would be the next number in the sequence and returned as the result. This process is then ...

  7. Heap's algorithm - Wikipedia

    en.wikipedia.org/wiki/Heap's_algorithm

    The induction proof for the claim is now complete, which will now lead to why Heap's Algorithm creates all permutations of array A. Once again we will prove by induction the correctness of Heap's Algorithm. Basis: Heap's Algorithm trivially permutes an array A of size 1 as outputting A is the one and only permutation of A.

  8. Maze generation algorithm - Wikipedia

    en.wikipedia.org/wiki/Maze_generation_algorithm

    This algorithm is a randomized version of Kruskal's algorithm. Create a list of all walls, and create a set for each cell, each containing just that one cell. For each wall, in some random order: If the cells divided by this wall belong to distinct sets: Remove the current wall. Join the sets of the formerly divided cells.

  9. Sethi–Ullman algorithm - Wikipedia

    en.wikipedia.org/wiki/Sethi–Ullman_algorithm

    The simple Sethi–Ullman algorithm works as follows (for a load/store architecture): . Traverse the abstract syntax tree in pre- or postorder . For every leaf node, if it is a non-constant left-child, assign a 1 (i.e. 1 register is needed to hold the variable/field/etc.), otherwise assign a 0 (it is a non-constant right child or constant leaf node (RHS of an operation – literals, values)).