value-iteration method - traducción al ruso
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value-iteration method - traducción al ruso

EIGENVALUE ALGORITHM
Power method; Von Mises iteration
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value-iteration method      

математика

итерационный метод

iterated function         
  • ''F''}}<br
/>is  '''iterated'''  indefinitely,   then  ''A ''  and  ''K''<br
/>are  the  starting  points  of  two  infinite  [[spiral]]s.
  • 6}}) is shown.
MATHEMATICAL OPERATION OF COMPOSING A FUNCTION WITH ITSELF REPEATEDLY
Picard sequence; Function iteration; Function Iteration; Iterated map; Fractional iteration; Iterative functional-differential equation; Iteration orbit; Iterative function

математика

итерированная функция

iteration algorithm         
NUMERICAL METHOD IN WHICH THE N-TH APPROXIMATION OF THE SOLUTION IS OBTAINED ON THE BASIS ON THE (N-1) PREVIOUS APPROXIMATIONS
Iterative methods; Krylov subspace methods; Krylov subspace method; Iterative approximation; Iteration scheme; Iterative algorithm; Iterative procedure; Iteration algorithm; Iteration methods; Iterative convergence; Iterative solver; Direct method (computational mathematics); Stationary iterative method; Iteration method

математика

итерационный алгоритм

Definición

value added
¦ noun Economics
1. the amount by which the value of an article is increased at each stage of its production, exclusive of initial costs.
2. the addition of features to a basic line or model for which the buyer is prepared to pay extra.

Wikipedia

Power iteration

In mathematics, power iteration (also known as the power method) is an eigenvalue algorithm: given a diagonalizable matrix A {\displaystyle A} , the algorithm will produce a number λ {\displaystyle \lambda } , which is the greatest (in absolute value) eigenvalue of A {\displaystyle A} , and a nonzero vector v {\displaystyle v} , which is a corresponding eigenvector of λ {\displaystyle \lambda } , that is, A v = λ v {\displaystyle Av=\lambda v} . The algorithm is also known as the Von Mises iteration.

Power iteration is a very simple algorithm, but it may converge slowly. The most time-consuming operation of the algorithm is the multiplication of matrix A {\displaystyle A} by a vector, so it is effective for a very large sparse matrix with appropriate implementation.

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