An eigenvector is a vector which, when transformed by a given matrix, is merely multiplied by a scalar constant; its direction isn't changed. An eigenvalue, in this context, is the factor by which the eigenvector is multiplied when transformed.
If a linear transformation acts on a vector and the result is only a change in the vector's magnitude, not direction, that vector is called an eigenvector of that particular linear transformation, and the magnitude that the vector is changed by is called an eigenvalue of that eigenvector.Formulaically, this statement is expressed as Av=kv, where A is the linear transformation, vis the eigenvector, and k is the eigenvalue. Keep in mind that A is usually a matrix and k is a scalar multiple that must exist in the field of which is over the vector space in question.
This is the definition of eigenvectors and eigenvalues according to Wikipedia:Specifically, a non-zero column vector v is a (right) eigenvector of a matrix A if (and only if) there exists a number λ such that Av = λv. The number λ is called the eigenvalue corresponding to that vector. The set of all eigenvectors of a matrix, each paired with its corresponding eigenvalue, is called the eigensystemof that matrix
Well in linear algebra if given a vector space V,over a field F,and a linear function A:V->V (i.e for each x,y in V and a in F,A(ax+y)=aA(x)+A(y))then ''e" in F is said to be an eigenvalue of A ,if there is a nonzero vector v in V such that A(v)=ev.Now since every linear transformation can represented as a matrix so a more specific definition would be that if u have an NxN matrix "A" then "e" is an eigenvalue for "A" if there exists an N dimensional vector "v" such that Av=ev.Basically a matrix acts on an eigenvector(those vectors whose direction remains unchanged and only magnitude changes when a matrix acts on it) by multiplying its magnitude by a certain factor and this factor is called the eigenvalue of that eigenvector.
An eigenvector of a square matrix Ais a non-zero vector v that, when the matrix is multiplied by v, yields a constant multiple of v, the multiplier being commonly denoted by lambda. That is: Av = lambdavThe number lambda is called the eigenvalue of A corresponding to v.
An eigenvector is a vector which, when transformed by a given matrix, is merely multiplied by a scalar constant; its direction isn't changed. An eigenvalue, in this context, is the factor by which the eigenvector is multiplied when transformed.
I'm seeking the answer too. What's the meaning of the principal eigenvector of an MI matrix?
If a linear transformation acts on a vector and the result is only a change in the vector's magnitude, not direction, that vector is called an eigenvector of that particular linear transformation, and the magnitude that the vector is changed by is called an eigenvalue of that eigenvector.Formulaically, this statement is expressed as Av=kv, where A is the linear transformation, vis the eigenvector, and k is the eigenvalue. Keep in mind that A is usually a matrix and k is a scalar multiple that must exist in the field of which is over the vector space in question.
This is a complicated subject, which can't be explained in a few words. Read the Wikipedia article on "eigenvalue"; or better yet, read a book on linear algebra. Briefly, and quoting from the Wikipedia, "The eigenvectors of a square matrix are the non-zero vectors that, after being multiplied by the matrix, remain parallel to the original vector. For each eigenvector, the corresponding eigenvalue is the factor by which the eigenvector is scaled when multiplied by the matrix."
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This is the definition of eigenvectors and eigenvalues according to Wikipedia:Specifically, a non-zero column vector v is a (right) eigenvector of a matrix A if (and only if) there exists a number λ such that Av = λv. The number λ is called the eigenvalue corresponding to that vector. The set of all eigenvectors of a matrix, each paired with its corresponding eigenvalue, is called the eigensystemof that matrix
Well in linear algebra if given a vector space V,over a field F,and a linear function A:V->V (i.e for each x,y in V and a in F,A(ax+y)=aA(x)+A(y))then ''e" in F is said to be an eigenvalue of A ,if there is a nonzero vector v in V such that A(v)=ev.Now since every linear transformation can represented as a matrix so a more specific definition would be that if u have an NxN matrix "A" then "e" is an eigenvalue for "A" if there exists an N dimensional vector "v" such that Av=ev.Basically a matrix acts on an eigenvector(those vectors whose direction remains unchanged and only magnitude changes when a matrix acts on it) by multiplying its magnitude by a certain factor and this factor is called the eigenvalue of that eigenvector.
An eigenvector of a square matrix Ais a non-zero vector v that, when the matrix is multiplied by v, yields a constant multiple of v, the multiplier being commonly denoted by lambda. That is: Av = lambdavThe number lambda is called the eigenvalue of A corresponding to v.
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