Lemma 1R:

If the rows of an n x n matrix A are linearly dependent, det(A)=0

Proof:

(1) Let A be an n x n matrix with rows that are linearly dependent.

[See Definition 1, here for review of linearly independent]

(2) Then, there exists α

_{i}such that:

α

_{1}R

_{1}+ ... + α

_{n}R

_{n}= 0.

(3) Then, it is possible to use elementary operations to set R

_{1}= 0 since:

(a) Multiply R

_{1}with α

_{1}

(b) For each row i (where i = 2 .. n), add α

_{i}*R

_{i}to R

_{1}.

(c) By step #2, it is clear after (a) and after each addition (b), R

_{1}= 0.

(4) So we have:

A

_{(0R1)}= E

_{n}E

_{n-1}...E

_{2}E

_{1}A where E

_{i}is an elementary operation. [See Theorem 5, here for review of how elementary operations map to multiplication]

(5) Using the definition of determinants [See Definition 4, here], we have:

det(A

_{(0R1)}) = 0 [See Lemma Corollary 4.2, here]

(6) Using our previous results, we know that all det(E

_{i}) ≠ 0. (See Lemma 4, Lemma 7, and Lemma 9 here)

(7) It therefore follows that det(A) = 0 since det(A

_{(0R1)}) = det(E

_{n})det(E

_{n-1})...det(E

_{2})det(E

_{1})det(A)

QED

Corollary 1.1R: If A is a matrix with two identical rows, then det(A)=0

Proof:

(1) Let A be an n x m matrix where row i = row j and i ≠ j.

(2) We can define the set α

_{k}such that:

if k = i, then α

_{k}=1

if k = j, then α

_{k}=-1

otherwise, α

_{k}=0

(3) Clearly, α

_{1}R

_{1}+ ... + α

_{n}R

_{n}=0 where not all α

_{k}= 0.

(4) Therefore, the rows of A of linearly dependent and using Lemma 1 above, det(A)=0.

QED

Lemma 1C:

If the columns of an n x n matrix A are linearly dependent, det(A)=0

Proof:

(1) Let A be an n x n matrix with columns that are linearly dependent.

[See Definition 1, here for review of linearly independent]

(2) Then, there exists α

_{i}such that:

α

_{1}C

_{1}+ ... + α

_{nC}

_{n}= 0.

(3) Then, it is possible to use elementary operations to set C

_{1}= 0 since:

(a) Multiply C

_{1}with α

_{1}

(b) For each column i (where i = 2 .. n), add α

_{i}*C

_{i}to C

_{1}.

(c) By step #2, it is clear after (a) and after each addition (b), C

_{1}= 0.

(4) So we have:

A

_{(0C1)}= AE

_{n}E

_{n-1}...E

_{2}E

_{1}where E

_{i}is an elementary operation. [See Theorem 5, here for review of how elementary operations map to multiplication]

(5) Using the definition of determinants [See Definition 4, here], we have:

det(A

_{(0C1)}) = 0 [See Lemma Corollary 4.2, here]

(6) Using our previous results, we know that all det(E

_{i}) ≠ 0. (See Lemma 4, Lemma 7, and Lemma 9 here)

(7) It therefore follows that det(A) = 0 since det(A

_{(0C1)}) = det(E

_{n})det(E

_{n-1})...det(E

_{2})det(E

_{1})det(A)

QED

Corollary 1.1C: If A is a matrix with two identical columns, then det(A)=0

Proof:

(1) Let A be an n x m matrix where row i = row j and i ≠ j.

(2) We can define the set α

_{k}such that:

if k = i, then α

_{k}=1

if k = j, then α

_{k}=-1

otherwise, α

_{k}=0

(3) Clearly, α

_{1}R

_{1}+ ... + α

_{n}R

_{n}=0 where not all α

_{k}= 0.

(4) Therefore, the rows of A of linearly dependent and using Lemma 1 above, det(A)=0.

QED

Lemma 2: det(A) is a linear function of the rows of A

That is,

if three matrices A,B,C are exactly the same except for one row where this row is α for A, β for B, and (α + β) for C, then:

det(C) = det(A) + det(B)

Proof:

(1) Assume that the rows which are the same in A,B, and C are linearly dependent. [See Definition 1, here]

(2) Then det(A)=0, det(B)=0, and det(C)=0. [See Lemma 1 above]

(3) So that we have 0 = det(C) = det(A) + det(B) = 0 + 0

(4) Assume that all the rows but the first row are the same and that none of these rows are linearly dependent.

(5) Let F

_{1 x n}be the set of all possible rows of length n.

(6) F

_{1 x n}is a vector space. [See Definition 2, here for definition of vector space]

(7) Since [ R

_{2}, R

_{3}, ..., R

_{n}] are linearly independent, we can use this list to build a basis: [ ε, R

_{2}, R

_{3}, ..., R

_{n}]. [See Theorem 1, here]

(8) Since [ ε, R

_{2}, R

_{3}, ..., R

_{n}] is a basis (see Definition 2, here for definition of basis), we have:

Row

_{1}(A) = α = a

_{1}ε + a

_{2}R

_{2}+ ... + a

_{n}R

_{n}

Row

_{1}(B) = β = b

_{1}ε + b

_{2}R

_{2}+ ... + b

_{n}R

_{n}

Row

_{1}(C) = (α + β) = (a

_{1}+ b

_{1})ε + (a

_{2}+ b

_{2})R

_{2}+ ... + (a

_{n}+ b

_{n})R

_{n}

(9) For A,B,C we can use I

_{(kRj + Ri)}to simplify the first row [See Definition 3, here for definition of A

_{(kRj + Ri)}].

For A, we set each k=a

_{i}and then use R

_{i}. In this way, we can reduce α to a

_{1}*ε. Likewise, we can reduce β to b

_{1}*ε and finally, we can reduce (α + β) to (a

_{1}+ b

_{1})ε.

(10) Since det(AB) = det(A)det(B) [See Definition 4, here] and since det(I

_{(kRj + Ri)}= 1 [See Lemma 7, here] , it follows that:

det(A) =

det(B) =

det(C) =

(11) It is also true that (see Definition 4, here):

det(A) =

det(B) =

det(C) =

(12) And it is true that:

det(C) = det(A) + det(B)

(13) Now, we have one last case to deal with. In this case, A,B,C are all the same except for a row r that is not the first row and all other rows are linearly independent.

(14) Now, from the elementary operations (see Lemma 9, here), we know that:

det(A*I

_{(Ri ↔ R1)}) = det(A)*(-1)

det(B*I

_{(Ri ↔ R1)}) = det(B)*(-1)

det(C*I

_{(Ri ↔ R1)}) = det(C)*(-1)

(15) Now from step #4 thru step #12, we know that:

det(C*I

_{(Ri ↔ R1)}) = det(A*I

_{(Ri ↔ R1)}) + det(B*I

_{(Ri ↔ R1)}) =

= -det(C) = -det(A) + -det(B)

(16) Multiplying both sides by -1 gives us:

det(C) = det(A) + det(B)

QED

Corollary 2.1: det(A) is a linear function of the rows of A

That is,

if three matrices A,B,C are exactly the same except for one column where this row is α for A, β for B, and (α + β) for C, then:

det(C) = det(A) + det(B)

Proof:

(1) Using Lemma 2 above, we have:

det(C

^{T}) = det(A

^{T}) + det(B

^{T})

(2) Using Theorem 9 here, we have:

det(C

^{T}) = det(C) det(A

^{T}) = det(A)' det(B

^{T}) = det (B)

(3) Putting this all together gives us:

det(C) = det(A) + det(B)

QED

Definition 1: Minor M

_{i,j}(A)

For an n x n matrix A, the minor of a

_{i,j}, M

_{i,j}(A), is the n-1 x n-1 submatrix of A obtained by deleting row i and column j of A.

Definition 2: Cofactor

For an n x n matrix A, the cofactor of a

_{i,j}, cof

_{i,j}(A), is defined as (-1)

^{i+j}det(M

_{i,j}(A)).

Lemma 3:

Let B be an F

_{n x n}matrix of the form:

then:

det(B) = det(M

_{1,1}(B)) = cof

_{1,1}(B).

Proof:

(1) Using the elementary row operation I

_{(kRj + Ri)}[See Definition 3, here] we can reduce B to the following form:

(2) Let E

_{1}*....*E

_{t}be a representation of M

_{1,1}(B) as a product of n-1 x n-1 elementary matrices. [See Theorem 6, here]

(3) Now, we have:

det(E

_{1})*...*det(E

_{t}) = det(E

_{1}*...*E

_{t}) = det(M

_{11}(B))

QED

Corollary 3.1:

Let B be an F

_{n x n}matrix of the form:

then:

det(B) = det(M

_{1,1}(B)) = cof

_{1,1}(B).

Proof:

(1) Using Lemma 3 above, we have:

det(B

^{T}) = det(M

_{1,1}(B)) = cof

_{1,1}(B).

(2) Using Theorem 9 here, we have:

det(B) = det(B

^{T})

QED

Theorem 4: Cofactor Expansion of rows

The determinant of an n x n matrix can be computed as the sum of products of the elements of any row and their cofactors.

That is:

det(A) = ∑(j=1,n) a

_{i,j}cof

_{i,j}(A)

Proof:

(1) Let A be an n x n matrix.

(2) Let i be the row that we wish to use to compute det(A).

(3) Using Row operation II (I

_{(Ri ↔ Rj)}), we move row i to row 1 by switching row i ↔ row (i-1) and row (i-1) ↔ row (i-2) ↔ ... ↔ row(2) ↔ row(1).

(4) We can see that there are i-1 such operations and at the end of it, we are left with the following order:

(5) Since det(I

_{(Ri ↔ Rj)}) = -1, the (i-1) row operations gives us:

(6) Using Lemma 2 above, we have:

det(A) =

(7) Using Column Operation II, we can move the nonzero column at column j (where j represents the current column) to column 1. This consists of the same type of operation as before:

column i ↔ column (j-1) and column (j-1) ↔ column (j-2) ↔ ... ↔ column(2) ↔ column(1).

(8) This gives us (-1)

^{0}for j=1, (-1)

^{1}for j=2, and so on so that we get:

det(A) = (-1)

^{(i-1)}[(-1)

^{(1-1)}det([a

_{i,1}*]) + (-1)

^{(2-1)}det([a

_{i,2}*]) + ... + (-1)

^{(n-1)}det([a

_{i,n}*]) ]

where [a

_{i,j}*] is the determinant of the [a

_{i,j}] after it has been moved to column 1 using column operations.

(9) Since (-1) to an even number = 1 and (-1) to an odd number = -1, we can simplify step #8 to the following:

det(A) = (-1)

^{i-1}[

]

(10) Using row operation I (I

_{(kRi)}), we further get:

det(A) = (-1)

^{i-1}[

]

(11) We can now plug in Lemma 3 above to complete the proof:

det(A) =

(-1)

^{i-1}[a

_{i,1}det(M

_{i,1}(A)) - a

_{i,2}det(M

_{i,2}(A)) + a

_{i,3}det(M

_{i,3}(A)) - a

_{i,4}det(M

_{i,4}(A)) + ...] =

(-1)

^{i-1}[a

_{i,1}cof

_{i,1}(A) - a

_{i,2}cof

_{i,2}(A) + a

_{i,3}cof

_{i,3}(A) - a

_{i,4}cof

_{i,4}(A) + ... ]

QED

The implication of Theorem 4 is that we can now establish a recursive method for obtaining the determinant of an n x n matrix.

Algorithm 1: Find the determininant of any n x n matrix

(1) If n x n is a 2 x 2 matrix, then the determinant is:

(2) If n x n is not a 2 x 2 matrix, then the determinant can be derived from the determinant of the Minor of n x n so that:

det(n x n) = a

_{1,1}det(M

_{1,1}) - a

_{1,2}det(M

_{1,2}) + ... + (-1)

^{(n-2)}a

_{1,(n-1)}det(M

_{1,(n-1)}+ (-1)

^{(n-1)}a

_{1,n}det(M

_{1,n}).

Corollary 4.1: Cofactor Expansion of columns

The determinant of an n x n matrix can be computed as the sum of products of the elements of any column and their cofactors.

That is:

det(A) = ∑(i=1,n) a

_{i,j}cofactor

_{i,j}(A)

Proof:

(1) Let A be an n x n matrix.

(2) Let j be the column that we wish to use to compute det(A).

(3) Using Column operation II (I

_{(Ci ↔ Cj)}), we move column j to column 1 by switching column j ↔ column (j-1) and column (j-1) ↔ column (j-2) ↔ ... ↔ column(2) ↔ column(1).

(4) We can see that there are j-1 such operations and at the end of it, we are left with the following order:

(5) Since det(I

_{(Ci ↔ Cj)}) = -1,

det(A) = (-1)

^{(j-1)}det([Col

_{j}Col

_{1}... Col

_{n}])

(6) Using Corollary 2.1 above, we have:

det(A) = (-1)

^{(j-1)}[det([a

_{1,j}]) + det([a

_{2,j}]) + ... + det([a

_{n,j}])]

where [a

_{i,j}] is the matrix derived from A where all values in column j are empty except for a

_{i,j}.

(7) Using Row Operation II (I

_{(Ri ↔ Rj)}), we can move this nonzero row at row i to row 1. This consists of the same type of operation as before:

row i ↔ row (i-1) and row (i-1) ↔ row (i-2) ↔ ... ↔ row(2) ↔ row(1).

(8) This gives us (-1)

^{0}for i=1, (-1)

^{1}for i=2, and so on so that we get:

det(A) = (-1)

^{(j-1)}[(-1)

^{(1-1)}det([a

_{1,j}*]) + (-1)

^{(2-1)}det([a

_{2,j}*]) + ... + (-1)

^{(n-1)}det([a

_{n,j}*]) ]

where [a

_{i,j}*] is the determinant of the [a

_{i,j}] after it has been moved to row 1 using row operations.

(9) Since (-1) to an even number = 1 and (-1) to an odd number = -1, we can simplify step #8 to the following:

det(A) = (-1)

^{(j-1)}[det([a

_{1,j}*]) - det([a

_{2,j}*]) + ... {+/-} det([a

_{n,j}*]) ]

where [a

_{i,j}*] is the determinant of the [a

_{i,j}] after it has been moved to row 1 using row operations.

(10) We can use column operation I (I

_{(kCi)})to convert each matrix into this form:

det(A) = (-1)

^{(j-1)}[a

_{1,j}det([a

_{i,j}**]) - a

_{2,j}det([a

_{2,1}**] + ... ]

where a

_{i,j}det([a

_{i,j}**]) = det([a

_{i,j}*])

(11) We can now plug in Corollary 3.1 above to complete the proof:

det(A) = (-1)

^{i-1}[a

_{i,1}det(M

_{i,1}(A)) - a

_{i,2}det(M

_{i,2}(A)) + a

_{i,3}det(M

_{i,3}(A)) - a

_{i,4}det(M

_{i,4}(A)) + ...] =

(-1)

^{i-1}[a

_{i,1}cof

_{i,1}(A) - a

_{i,2}cof

_{i,2}(A) + a

_{i,3}cof

_{i,3}(A) - a

_{i,4}cof

_{i,4}(A) + ... ]

QED

References

- Charles G. Cullen, Matrices and Linear Transformations, Dover Publications, Inc., 1972.

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