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  2. CYK algorithm - Wikipedia

    en.wikipedia.org/wiki/CYK_algorithm

    Now the sentence she eats a fish with a fork is analyzed using the CYK algorithm. In the following table, in P [ i , j , k ] {\displaystyle P[i,j,k]} , i is the number of the row (starting at the bottom at 1), and j is the number of the column (starting at the left at 1).

  3. Fast marching method - Wikipedia

    en.wikipedia.org/wiki/Fast_marching_method

    The fast marching method [1] is a numerical method created by James Sethian for solving boundary value problems of the Eikonal equation: | | = / () =Typically, such a problem describes the evolution of a closed surface as a function of time with speed in the normal direction at a point on the propagating surface.

  4. How to Solve It - Wikipedia

    en.wikipedia.org/wiki/How_to_Solve_It

    How to Solve It suggests the following steps when solving a mathematical problem: . First, you have to understand the problem. [2]After understanding, make a plan. [3]Carry out the plan.

  5. Fisher–Yates shuffle - Wikipedia

    en.wikipedia.org/wiki/Fisher–Yates_shuffle

    Repeat from step 2 until all the numbers have been struck out. The sequence of numbers written down in step 3 is now a random permutation of the original numbers. Provided that the random numbers picked in step 2 above are truly random and unbiased, so will be the resulting permutation.

  6. MacCormack method - Wikipedia

    en.wikipedia.org/wiki/MacCormack_method

    The application of MacCormack method to the above equation proceeds in two steps; a predictor step which is followed by a corrector step. Predictor step: In the predictor step, a "provisional" value of u {\displaystyle u} at time level n + 1 {\displaystyle n+1} (denoted by u i p {\displaystyle u_{i}^{p}} ) is estimated as follows

  7. Gradient descent - Wikipedia

    en.wikipedia.org/wiki/Gradient_descent

    The idea is to take repeated steps in the opposite direction of the gradient (or approximate gradient) of the function at the current point, because this is the direction of steepest descent. Conversely, stepping in the direction of the gradient will lead to a trajectory that maximizes that function; the procedure is then known as gradient ascent .

  8. Algorithm - Wikipedia

    en.wikipedia.org/wiki/Algorithm

    Flowchart of using successive subtractions to find the greatest common divisor of number r and s. In mathematics and computer science, an algorithm (/ ˈ æ l ɡ ə r ɪ ð əm / ⓘ) is a finite sequence of mathematically rigorous instructions, typically used to solve a class of specific problems or to perform a computation. [1]

  9. Semidefinite programming - Wikipedia

    en.wikipedia.org/wiki/Semidefinite_programming

    A linear programming problem is one in which we wish to maximize or minimize a linear objective function of real variables over a polytope.In semidefinite programming, we instead use real-valued vectors and are allowed to take the dot product of vectors; nonnegativity constraints on real variables in LP (linear programming) are replaced by semidefiniteness constraints on matrix variables in ...