Determinants
Contents
Definition of detA
Permutations
A permutation of (1,2,…,n) is a sequence of n integers i1,…,in∈{1,…,n} such that ik=il for all k=l.
For example, all possible permutations of (1,2,3) are
(1,2,3),(1,3,2),(2,1,3),(2,3,1),(3,1,2),(3,2,2).
There are n! permutations of (1,2,…,n).
Sign of a permutation
By definition, the number of inversions of a permutation (i1,…,in) is
δ(i1,…,in)=def#{k<l∣ik>il}
A permutation (i1,i2,…,in) is called even if δ(i1,…,in) is even.
A permutation (i1,i2,…,in) is called odd if δ(i1,…,in) is odd.
The sign of the permutation (i1,…,in) is defined to be
ϵi1i2⋯in=(−1)δ(i1,i2,…,in)={+1−1if δ(i1,…,in) is evenif δ(i1,…,in) is odd
The quantity ϵi1i2⋯in is called the Levi-Civita symbol.
The determinant
detA=defi1,…,in∑ϵi1i2⋯ina1i1a2i2⋯anin
Properties of the sign ϵi1i2⋯in
- ϵi1⋯ik⋯iℓ⋯in=−ϵi1⋯iℓ⋯ik⋯in
- if i1,…,ik<ik+1,…,in then ϵi1i2⋯in=ϵi1⋯ikϵik+1⋯in
What is the sign of a14a22a35a43a51 when you expand a 5×5 determinant
The question can be rephrased as: compute ϵ42531
∣∣∣∣∣∣∣∣∣∣∣a11a21a31a41a51a12a22a32a42a52a13a23a33a43a53a14a24a34a44a54a15a25a35a45a55∣∣∣∣∣∣∣∣∣∣∣
Special cases
2×2 determinants
∣∣∣∣∣acbd∣∣∣∣∣=ad−bc
3×3 determinants
∣∣∣∣∣∣∣a1b1c1a2b2c2a3b3c3∣∣∣∣∣∣∣=a1b2c3−a1b3c2−a2b1c3+a2b3c1+a3b1c2−a3b2c1
Determinant of upper triangular matrices
If all entries of a matrix below its diagonal are zero, then the determinant of the matrix is the product of its diagonal entries:
∣∣∣∣∣∣∣∣∣∣∣a1100⋯0a12a220⋯0⋯⋯a33⋯⋯⋯⋯⋯⋯0a1na2na3n⋯ann∣∣∣∣∣∣∣∣∣∣∣=a11a22⋯ann
Determinant of the identity matrix
detI=1.
Determinant of block triangular matrices
If A is a k×k matrix, B an k×l matrix, and C an l×l matrix then
∣∣∣∣∣A0BC∣∣∣∣∣=(detA)(detC)
Properties
Determinant of the transpose
detA⊤=detA
Swapping rows or columns changes the sign
If B is the matrix you get by swapping two rows, or by swapping two columns in the matrix A, then detA=−detB
The determinant as a function of its rows
If we have n row vectors a1,…,an∈Fn, given by
a1a2an=(a11a12⋯a1n),=(a21a22⋯a2n),⋮=(an1an2⋯ann),
then we define
det(a1,a2,…,an)=∣∣∣∣∣∣∣∣∣∣a11a21⋮an1a12a22⋮an2⋯⋯⋯a1na2n⋮ann∣∣∣∣∣∣∣∣∣∣
One has for all a,b,a1,a2,…,an∈Fn and t∈F:
det(a+b,a2,a3,…,an)det(ta,a2,a3,…,an)det(a1,…,ai,…,aj,…an)det(a1,…,ai,…,ai,…an)det(a1,…,ai,…,aj,…an)=det(a,a2,a3,…,an)+det(b,a2,a3,…,an)=tdet(a,a2,a3,…,an)=−det(a1,…,aj,…,ai,…an)=0=det(a1,…,ai+taj,…,aj,…an)
Consequence: if {a1,…,an} is linearly dependent, then det(a1,…,an)=0
Cofactor expansion
The ij-minor of an n×n matrix A is the (n−1)×(n−1) matrix obtained by deleting the ith row and jth column from A. Our textbook uses the notation A~ij for the ij-minor of A.
The ij-cofactor of the matrix A is the number
cij=(−1)i+jdetA~ij
Cofactor Expansion Theorem. If A=(aij) is an n×n matrix, then one has
detA=ai1ci1+ai2ci2+⋯+aincin
for any i∈{1,2,…,n}.
A consequence of the cofactor expansion theorem is that if i=j then
aj1ci1+aj2ci2+⋯+ajncin=0.
Example
Expanding a 3×3 determinant along its middle row:
∣∣∣∣∣∣∣13−2224313∣∣∣∣∣∣∣=a21c21+a22c22+a23c23=−3⋅∣∣∣∣∣2413∣∣∣∣∣+2⋅∣∣∣∣∣1−233∣∣∣∣∣−1⋅∣∣∣∣∣1−224∣∣∣∣∣=⋯
Theorem. For any n×n matrix A one has
AC⊤=C⊤A=(detA)I,
where C is the cofactor matrix of A.
If detA=0 then A is invertible, and the inverse matrix is given by
A−1=detA1C⊤.
Proof
Let A=(aij) and C=(cij) where cij=(−1)i+jdetA~ij.
Consider B=AC⊤. By definition of matrix multiplication you have
Bij=ai1cj1+⋯+aincjn
The expression on the right is what we get if we replace the jth row of the matrix A with (ai1⋯ain), i.e. with the ith row of A.
If i=j then rows i and j in the resulting determinant are equal, so the determinant vanishes: Bij=0 for all i=j.
If i=j then replacing row j with row i does nothing, and we just get detA: you get Bii=detA for all i.
The result is
AC⊤=⎝⎜⎜⎜⎜⎜⎛detA0000detA0000detA0⋯⋯⋯⋱000detA⎠⎟⎟⎟⎟⎟⎞=(detA)I.
Cramer's rule.
For any y∈Fn the solution of Ax=y is given by
x1=∣∣∣∣∣∣∣∣∣a11a12a1na12a22an2…………a1na2nann∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣y1y2yna12a22an2…………a1na2nann∣∣∣∣∣∣∣∣∣,x2=∣∣∣∣∣∣∣∣∣a11a12a1na12a22an2…………a1na2nann∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣a11a12a1ny1y2yn…………a1na2nann∣∣∣∣∣∣∣∣∣,…,xn=∣∣∣∣∣∣∣∣∣a11a12a1na12a22an2…………a1na2nann∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣a11a12a1na12a22an2…………y1y2yn∣∣∣∣∣∣∣∣∣,
Proof
We compute C⊤y:
C⊤y=⎝⎜⎜⎛c11⋮c1nc21c2n⋯⋯cn1⋮cnn⎠⎟⎟⎞⎝⎜⎜⎛y1⋮yn⎠⎟⎟⎞=⎝⎜⎜⎛y1c11+⋯+yncn1⋮y1c1n+⋯+yncnn⎠⎟⎟⎞
Our formula for the inverse of A implies that the solution x=A−1y is given by
x=detA1⎝⎜⎜⎛y1c11+⋯+yncn1⋮y1c1n+⋯+yncnn⎠⎟⎟⎞
Thus the ith component x1 of the solution is given by
xi=detAy1c1i+⋯+yncni.
The numerator in this fraction is the determinant that we get by replacing the ith column in detA with the column vector y.
Example the inverse of a 2×2 matrix
(acbd)−1=ad−bc1(d−c−ba)
Determinant of a matrix product
Theorem. For any two n×n matrices A and B one has
det(AB)=det(A)det(B)
Proof
The short version of the proof uses block matrix notation and goes like this:
det(A)det(B)=∣∣∣∣∣A−I0B∣∣∣∣∣(add the first n columns B timesto the second n columns)=∣∣∣∣∣A−IAB0∣∣∣∣∣=(−1)n∣∣∣∣∣−I0AAB∣∣∣∣∣=(−1)ndet(−I)det(AB)=det(AB)
If we avoid block matrix notation then we get the following more detailed version of the computation. By definition,
∣∣∣∣∣A−I0B∣∣∣∣∣=∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣a11a21an1−100a12a22an20−10⋯⋯⋮⋯⋯⋮⋯a1na2nann00⋮−1000b11b21⋮bn100⋱0b12b22bn2⋯⋯⋯⋯⋯⋮⋯000b1nb2nbnn∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣.
Add the first column b11 times to column (n+1), the second column b12 times to column (n+2), etc, clearing out the top row in the b section. The result is:
∣∣∣∣∣A−I0B∣∣∣∣∣=∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣a11a21an1−100a12a22an20−10⋯⋯⋮⋯⋯⋮⋯a1na2nann00⋮−1a11b11a21b11an1b110b21⋮bn1a11b12a21b12⋱an1b120b22bn2⋯⋯⋯⋯⋯⋮⋯a11b1na21b1nan1b1n0b2nbnn∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣
Next clear out the second row in the b-section. We get
∣∣∣∣∣A−I0B∣∣∣∣∣=∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣a11a21an1−100a12a22an20−10⋯⋯⋮⋯⋯⋮⋯a1na2nann00⋮−1a11b11+a12b21a21b11+a22b21an1b11+an2b2100⋮bn1⋯⋯⋱⋯⋯⋯⋯a11b1n+a12b21a21b1n+a22b21an1b1n+an2b2100⋮bnn∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣
Repeating this for the remaining rows in the b-section of the determinant, we end up with
∣∣∣∣∣A−I0B∣∣∣∣∣=∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣a11a21an1−100a12a22an20−10⋯⋯⋮⋯⋯⋮⋯a1na2nann00⋮−1a11b11+⋯+a1nbn1a21b11+⋯+a2nbn1an1b11+⋯+annbn100⋮0⋯⋯⋱⋯⋯⋯⋯a11b1n+⋯+a1nbnna21b1n+⋯+a2nbnnan1b1n+⋯+annbnn00⋮0∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣
i.e. we have
∣∣∣∣∣A−I0B∣∣∣∣∣=∣∣∣∣∣A−IAB0∣∣∣∣∣.
Theorem. A is invertible ⟺detA=0
Proof
If A is invertible then the matrix B=A−1 satisfies AB=I. This implies det(AB)=1 and hence (detA)(detB)=1. Therefore detA=0.
Conversely, if detA=0 then A is invertible because we have a formula for its inverse, A−1=detAC⊤.
Theorem. If a1,…,an∈Fn then
{a1,…,an} is linearly independent ⟺det(a1,…,an)=0
Proof
Consider the matrix A whose columns are a1,…,an. Then for any c1,…,cn∈F we have
c1a1+⋯+cnan=A⎝⎜⎜⎛c1⋮an⎠⎟⎟⎞.
detA=0⟹{a1,…,an} independent.
If detA=0 then A is invertible. This implies that N(A)={0}. Suppose that there are c1,…,cn∈F with c1a1+⋯+cnan=0. Then c=(c1⋮cn) satisfies Ac=0, so c∈N(A). Therefore c=0, i.e. c1=⋯=cn=0. This implies that {a1,…,an} is independent.
{a1,…,an} independent implies detA=0.
Any c∈N(A) satisfies c1a1+⋯+cnan=0. Since {a1,…,an} is independent this implies c1=⋯=cn=0, i.e. c=0. We conclude that N(A)={0}.
It follows that A is injective, and hence also that A is bijective (because of the Bijectivity Theorem). Bijective means the same as invertible, so A is invertible, and thus detA=0.