We have defined the determinant of a matrix to be a linear function of its
rows or columns whose magnitude is the hypervolume of the region with edges given
by its columns, or by its rows.
The determinant has a number of important properties as follows:
1. Linearity in columns: If we have column n-vectors c(k) and d(k), for k =
1 to n, and pick any j in this range then the n dimensional determinant obeys
the condition
det (c(1),
c(j - 1), a*c(j) + b*d(j), c(j + 1),
,c(n)) =
a*det (c(1),
c(j - 1), c(j), c(j + 1),...,c(n)) +
b*det (c(1),
c(j-1), d(j), c(j + 1),...,c(n)).
2. Linearity in rows: write this one out yourself.
3. The determinant is 0 if two columns are the same. (likewise for rows) Equivalently,
it changes sign if you interchange two rows (or columns).
4. The determinant can be evaluated by a process like row reduction.
5. The determinant of the matrix product of two matrices
is the product of their determinants.
6. In terms of the elements of a matrix M in any one column say M1j,
M2j, ...
the determinant can be expressed as
det M = M1j*C(1, j) + M2j*C(2, j) + ...
The quantities C(i, j) that occur here are called cofactors of the matrix M.
C(i, j) must be linear in all the rows of M except the i-th and in all the columns
of M except the j-th, and it must be 0 if two of those rows or columns are the
same; so it is proportional to the determinant of the matrix obtained by removing
the i-th row and j-th column from M. The proportionality constant turns out to
be (-1)i+j.
7. The inverse of the matrix M is the matrix whose (i, j)-th element is .
8. If you have a set of equations of the form Mv
= c, then the i-th component of v is given by the ratio of the determinant
of the matrix obtained by taking M and substituting c for the i-th column
of M, divided by the determinant of M itself. (This statement is called Cramer's
Rule)
9. The condition that the determinant of a matrix is 0 means that the hypervolume
of the region determined by the columns is 0 which means that they are linearly
dependent, and it means that there is a non-zero linear combination of the columns
that is the zero vector. Which means that for this vector v, we have Mv
= (0).
10. The determinant is unchanged by rotations of coordinates.
11. The polynomial of degree n in x defined by det(M - xI) is called the characteristic
polynomial of M. Its roots (solutions to it = 0) are called the eigenvalues of
M.
We now comment on these claims.
The first three follow immediately from the definition of the determinant as a
linear version of hypervolume.
It follows from these that you can add a multiple of one row to another without
changing the determinant: because by linearity the change would have to be a multiple
of the determinant of a matrix with two identical rows. But then you can do this
until the matrix is diagonal, at which point the determinant, again by linearity,
is the product of the diagonal elements times the determinant of the identity
matrix (which is 1).
The statement that the determinant of a product of two matrices is the product
of the determinants is important and useful. It follows by these two observations:
1. If the matrix A is diagonal, then det A is the product of the diagonal elements
of A. On the other hand, the rows of AB are just the rows of B each multiplied
by the corresponding diagonal element of A. By linearity then, the determinant
of AB is the product of the diagonal elements of A times the determinant of B,
that is, it is the product of the determinant of A and that of B, as we have claimed.
2. If we apply row operations as just discussed on A to obtain a new matrix
A' and apply the same row operation to (AB) to obtain (AB)' we will have
A'B = (AB)' and we will have det A = det A' , and det AB = det A'B.
We can do this until A is diagonal, at which point we can use the first remark
to tell us that (det A') * det B = det A'B, from which our conclusion follows.
The statements about cofactors merely make explicit what it means to be linear
in each row and column. The sign factor can be deduced from the fact that it is
1 if you consider the first row and column, (think of the identity matrix) and
you can switch rows and columns with their neighbors i - 1 and j - 1 times to
rearrange things so that the i-th row and j-th column become the first and everything
else is in their original orders. This will cause i + j - 2 sign changes, which
gives the sign factor noted.
As already noted somewhere the inverse statement is a statement about the dot
products of the rows of the inverse with the columns of the original matrix. The
diagonal products must be 1 which follows from the cofactor formula for the determinant,
and the off diagonal ones must be zero because by that same formula they represent
the determinants of matrices with two identical columns or rows.
Cramer's rule is the observation that by the definition of the inverse, the
desired coefficient is the dot product of the i-th row of the inverse of M with
the vector c. But by the cofactor formula this is dot product of the i-th column
of the cofactor matrix with the vector c, divided by the determinant of M, and
that is the ratio of the two determinants of Cramer's rule.
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