Search results
Results from the WOW.Com Content Network
Each Lagrange basis polynomial () can be rewritten as the product of three parts, a function () = common to every basis polynomial, a node-specific constant = (called the barycentric weight), and a part representing the displacement from to : [4]
Left to right steps indicate addition whereas right to left steps indicate subtraction; If the slope of a step is positive, the term to be used is the product of the difference and the factor immediately below it. If the slope of a step is negative, the term to be used is the product of the difference and the factor immediately above it.
Lagrange interpolation allows computing a polynomial of degree less than n that takes the same value at n given points as a given function. Instead, Hermite interpolation computes a polynomial of degree less than n such that the polynomial and its first few derivatives have the same values at m (fewer than n ) given points as the given function ...
The Euler–Lagrange equations can also be formulated in terms of the generalized momenta rather than generalized coordinates. Performing a Legendre transformation on the generalized coordinate Lagrangian L ( q , d q /d t , t ) obtains the generalized momenta Lagrangian L ′( p , d p /d t , t ) in terms of the original Lagrangian, as well the ...
However, there exist polynomial time algorithms to find a basis with defect () where c is some constant depending only on the number of basis vectors and the dimension of the underlying space (if different) [citation needed]. This is a good enough solution in many practical applications [citation needed].
In matrix theory, Sylvester's formula or Sylvester's matrix theorem (named after J. J. Sylvester) or Lagrange−Sylvester interpolation expresses an analytic function f(A) of a matrix A as a polynomial in A, in terms of the eigenvalues and eigenvectors of A. [1] [2] It states that [3]
The finite difference coefficients for a given stencil are fixed by the choice of node points. The coefficients may be calculated by taking the derivative of the Lagrange polynomial interpolating between the node points, [3] by computing the Taylor expansion around each node point and solving a linear system, [4] or by enforcing that the stencil is exact for monomials up to the degree of the ...
In other words, the interpolation polynomial is at most a factor Λ n (T ) + 1 worse than the best possible approximation. This suggests that we look for a set of interpolation nodes with a small Lebesgue constant. The Lebesgue constant can be expressed in terms of the Lagrange basis polynomials: