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Prove that a matrix a is singular if and only if it has a zero eigenvalue?
Wiki User
∙ 13y ago
Recall that if a matrix is singular, it's determinant is zero. Let our nxn matrix be called A and let k stand for the eigenvalue. To find eigenvalues we solve the equation det(A-kI)=0for k, where I is the nxn identity matrix.
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Assume that k=0 is an eigenvalue. Notice that if we plug zero into this equation for k, we just get det(A)=0. This means the matrix is singluar.
(==>)
Assume that det(A)=0. Then as stated above we need to find solutions of the equation det(A-kI)=0. Notice that k=0 is a solution since det(A-(0)I) = det(A) which we already know is zero. Thus zero is an eigenvalue.
Wiki User
∙ 13y ago
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What is the determinant rank of the determinant of 123456 its a 2 x 3 matrix?
A determinant is defined only for square matrices, so a 2x3 matrix does not have a determinant.Determinants are defined only for square matrices, so a 2x3 matrix does not have a determinant.
Why do square matrices only have multiplicative inverses?
there are pseudo inverses for non-square matrices a square matrix has an inverse only if the original matrix has full rank which implies that no vector is annihilated by the matrix as a multiplicative operator
Can a matrix of 4x5 be added with a matrix with dimensions of 5x3?
No. You can only add matrices of the same size.
How do you find inverse of matrix by elementary transformation?
Starting with the square matrix A, create the augmented matrix AI = [A:I] which represents the columns of A followed by the columns of I, the identity matrix.Using elementary row operations only (no column operations), convert the left half of the matrix to the identity matrix. The right half, which started off as I, will now be the inverse of A.Starting with the square matrix A, create the augmented matrix AI = [A:I] which represents the columns of A followed by the columns of I, the identity matrix.Using elementary row operations only (no column operations), convert the left half of the matrix to the identity matrix. The right half, which started off as I, will now be the inverse of A.Starting with the square matrix A, create the augmented matrix AI = [A:I] which represents the columns of A followed by the columns of I, the identity matrix.Using elementary row operations only (no column operations), convert the left half of the matrix to the identity matrix. The right half, which started off as I, will now be the inverse of A.Starting with the square matrix A, create the augmented matrix AI = [A:I] which represents the columns of A followed by the columns of I, the identity matrix.Using elementary row operations only (no column operations), convert the left half of the matrix to the identity matrix. The right half, which started off as I, will now be the inverse of A.
Can a matrix with dimensions of 2 X 4 be added to another matrix with dimensions of 2 X 5?
No. Matrix addition (or subtraction) is defined only for matrices of the same dimensions.
What is the eigen value?
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
What is a non-singuar matrix?
A non-singular matrix is basically one that has a multiplicative inverse. More specifically, a matrix "A" is non-singular if there is a matrix "B", such that AB = BA = 1, where "1" is the unity matrix. Non-singular matrixes are those that have a non-zero determinant. Singular and non-singular matrixes are only defined for square matrixes.
Why singular matrix should have non zero vector?
There is no reason why it should! So the question is based on an incorrect assumption. A matrix of only zero vectors will be singular!
Is eigenvalue applicable only to n x n matrices?
The answer is yes, and here's why: Remember that for the eigenvalues (k) and eigenvectors (v) of a matrix (M) the following holds: M.v = k*v, where "." denotes matrix multiplication. This operation is only defined if the number of columns in the first matrix is equal to the number of rows in the second, and the resulting matrix/vector will have as many rows as the first matrix, and as many columns as the second matrix. For example, if you have a 3 x 2 matrix and multiply with a 2 x 4 matrix, the result will be a 3 x 4 matrix. Applying this to the eigenvalue problem, where the second matrix is a vector, we see that if the matrix M is m x n and the vector is n x 1, the result will be an m x 1 vector. Clearly, this can never be a scalar multiple of the original vector.
What is an eigenvalue?
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.
What does eigenvalues mean?
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.
Does maximizing the scalar multiplication in matrix chain multiplication exhibit optimal substructure?
I think so. Copy and paste method could be used to prove this. But this is only my opinion.
If a matrix has only one element is it a square matrix?
Yes.
What is the difference between zero matrix and null matrix?
there is none you weasel. the only good matrix is revolutions. :)
Can a nonsquare matrix be a triangular matrix?
No. Only square matrices can be triangular.
Is the scalar matrix is always a identity matrix?
No. A scalar matrix is a diagonal matrix whose main diagonal elements are the same. Only if the diagonal elements are all 1 is it an identity matrix.
What is the determinant rank of the determinant of 123456 its a 2 x 3 matrix?
A determinant is defined only for square matrices, so a 2x3 matrix does not have a determinant.Determinants are defined only for square matrices, so a 2x3 matrix does not have a determinant.
