Can a matrix have more than one determinant?

Thus, the value of the determinant of of every matrix is determined by the definition. There can be only one determinant function.

If two determinants differ by just one column, we can add them together by just adding up these two columns. For example: … All other elementary row operations will not affect the value of the determinant!

Thus, both the matrices have the same determinant value. Hence, we cay say, two different matrices can have the same determinant value.

The answer is “NO”. The determinant only exists for square matrices.

Since a determinant stays the same by interchaning the rows and columns, it should be obvious that similar to ‘row-by-row’ multiplication that we’ve encountered above, we can also have ‘row-by-column’ multiplication and ‘column-by-column’ multiplication.

Therefore, when we add a multiple of a row to another row, the determinant of the matrix is unchanged. Note that if a matrix A contains a row which is a multiple of another row, det(A) will equal 0.

You have to split the given determinant into a sum of two determinants using the splitting of terms in one row at a time keeping the other rows intact, each of which then gives a sum of two determinants using the splitting of the terms of the next row.

A matrix is said to be singular if and only if its determinant is equal to zero. A singular matrix is a matrix that has no inverse such that it has no multiplicative inverse.

Determinants of equivalent matrices are not same in general but here you are adding rows ,therefore determinant remains same. Yes, if two matrices are similar then their trace/rank/determinant/eigenvalue will be equal.

Matrices; Determinants and Eigenvalues: Given a square real matrix A its determinant det(A) is the product of the eigenvalues of A . Because of this, two matrices with the same eigenvalues have the same determinant.

If a matrix has the same number of rows and columns (e.g., if m == n), the matrix is square. The definitions that follow in this section only apply to square matrices.

1. Pick any row or column in the matrix. It does not matter which row or which column you use, the answer will be the same for any row. …
2. Multiply every element in that row or column by its cofactor and add. The result is the determinant.

Difference between Matrix and Determinant: … A matrix is a group of numbers but a determinant is a unique number related to that matrix. In a matrix the number of rows need not be equal to the number of columns whereas, in a determinant, the number of rows should be equal to the number of columns.

If either two rows or two columns are identical, the determinant equals zero. If a matrix contains either a row of zeros or a column of zeros, the determinant equals zero.

The effect of scaling a matrix. Since a linear transformation can always be written as T(x)=Ax for some matrix A, applying a linear transformation to a vector x is the same thing as multiplying by a matrix. … In one dimension, the effect of doubling every vector would simply double the expansion of length by ˜T.

Exchanging two rows, or two columns of a matrix switches the sign of the determinant. For a fun corollary this means any matrix that has two rows or columns that are the same must have zero determinant.

Matrix multiplication is not commutative.

If A is an n × n matrix, and k is any constant, then detkA = kn detA. When using the theorem, it is important to keep in mind that the constant k in the determinant formula gets multiplied by itself n times, since each of the n rows of A was multiplied by k.

Proof by induction that transposing a matrix does not change its determinant.

If the determinant of a matrix is 0, the matrix is said to be singular, and if the determinant is 1, the matrix is said to be unimodular.

determinant: The unique scalar function over square matrices which is distributive over matrix multiplication, multilinear in the rows and columns, and takes the value of 1 for the unit matrix. Its abbreviation is “det “.

The determinant of the inverse of an invertible matrix is the inverse of the determinant: det(A-1) = 1 / det(A) [6.2. 6, page 265]. Similar matrices have the same determinant; that is, if S is invertible and of the same size as A then det(S A S-1) = det(A).

In matrix calculus, Jacobi’s formula expresses the derivative of the determinant of a matrix A in terms of the adjugate of A and the derivative of A. where tr(X) is the trace of the matrix X. It is named after the mathematician Carl Gustav Jacob Jacobi.

In a determinant the sum of the product of the elements of any row (or column) with the cofactors of the corresponding elements of any other row (or column) is zero.

We determine whether a matrix is a singular matrix or a non-singular matrix depending on its determinant. The determinant of a matrix ‘A’ is denoted by ‘det A’ or ‘|A|‘. If the determinant of a matrix is 0, then it is said to be a singular matrix.

Since A is a singular matrix. So det A = 0. FINAL ANSWER. Hence the required value of a = 4.

1. If the determinant is equal to , the matrix is singular.
2. If the determinant is non-zero, the matrix is non-singular.

A matrix’s determinant gives the volume of the parallelepiped whose sides are the columns of the matrix. If two matrices with identical dimensions have the same determinant, it means their corresponding parallelepipeds have the same volume.

The matrices can be transformed into one another by a combination of elementary row and column operations. Two matrices are equivalent if and only if they have the same rank.

(a) Find a matrix B in reduced row echelon form such that B is row equivalent to the matrix A. … Condition that Two Matrices are Row Equivalent We say that two m×n matrices are row equivalent if one can be obtained from the other by a sequence of elementary row operations. Let A and I be 2×2 matrices defined as follows.

Two matrices are equal if all three of the following conditions are met: Each matrix has the same number of rows. Each matrix has the same number of columns. Corresponding elements within each matrix are equal.

However, since all non-square matrices do not have a unique inverse, it was not useful to define the determinant for non-square matrices. Only the square matrices require knowledge of whether they have a unique inverse or not. So the determinant was only defined for square matrices as a result.

1. The definition of a matrix inverse requires commutativity—the multiplication must work the same in either order.
2. To be invertible, a matrix must be square, because the identity matrix must be square as well.

Yes, the determinant of a matrix can be a negative number. By the definition of determinant, the determinant of a matrix is any real number. Thus, it includes both positive and negative numbers along with fractions.

1.5 So, by calculating the determinant, we get det(A)=ad-cb, Simple enough, now lets take AT (the transpose). 1.8 So, det(AT)=ad-cb. 1.9 Well, for this basic example of a 2×2 matrix, it shows that det(A)=det(AT).

Not all 2 × 2 matrices have an inverse matrix. If the determinant of the matrix is zero, then it will not have an inverse; the matrix is then said to be singular. Only non-singular matrices have inverses.