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  2. Eigenfunction - Wikipedia

    en.wikipedia.org/wiki/Eigenfunction

    Its eigenfunctions form a basis of the function space on which the operator is defined [5] As a consequence, in many important cases, the eigenfunctions of the Hermitian operator form an orthonormal basis. In these cases, an arbitrary function can be expressed as a linear combination of the eigenfunctions of the Hermitian operator.

  3. Separation of variables - Wikipedia

    en.wikipedia.org/wiki/Separation_of_variables

    −λ here is the eigenvalue for both differential operators, and T(t) and X(x) are corresponding eigenfunctions. We will now show that solutions for X(x) for values of λ ≤ 0 cannot occur: Suppose that λ < 0. Then there exist real numbers B, C such that

  4. Eigenvalues and eigenvectors of the second derivative

    en.wikipedia.org/wiki/Eigenvalues_and...

    These formulas are used to derive the expressions for eigenfunctions of Laplacian in case of separation of variables, as well as to find eigenvalues and eigenvectors of multidimensional discrete Laplacian on a regular grid, which is presented as a Kronecker sum of discrete Laplacians in one-dimension.

  5. Spectral theory of ordinary differential equations - Wikipedia

    en.wikipedia.org/wiki/Spectral_theory_of...

    This function ω(λ) plays the role of the characteristic polynomial of D. Indeed, the uniqueness of the fundamental eigenfunctions implies that its zeros are precisely the eigenvalues of D and that each non-zero eigenspace is one-dimensional.

  6. Functional principal component analysis - Wikipedia

    en.wikipedia.org/wiki/Functional_principal...

    Functional principal component analysis (FPCA) is a statistical method for investigating the dominant modes of variation of functional data.Using this method, a random function is represented in the eigenbasis, which is an orthonormal basis of the Hilbert space L 2 that consists of the eigenfunctions of the autocovariance operator.

  7. Eigenvalues and eigenvectors - Wikipedia

    en.wikipedia.org/wiki/Eigenvalues_and_eigenvectors

    Using the Leibniz formula for determinants, the left-hand side of equation is a polynomial function of the variable λ and the degree of this polynomial is n, the order of the matrix A. Its coefficients depend on the entries of A, except that its term of degree n is always (−1) n λ n. This polynomial is called the characteristic polynomial of A.

  8. This Is the 1 Step You Should Never Skip When Baking Cookies

    www.aol.com/1-step-never-skip-baking-143000518.html

    It’s the key to soft, chewy, and flavorful cookies.

  9. Kosambi–Karhunen–Loève theorem - Wikipedia

    en.wikipedia.org/wiki/Kosambi–Karhunen–Loève...

    The covariance function K X satisfies the definition of a Mercer kernel. By Mercer's theorem, there consequently exists a set λ k, e k (t) of eigenvalues and eigenfunctions of T K X forming an orthonormal basis of L 2 ([a,b]), and K X can be expressed as (,) = = ()