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Cofactors

A computer finds the determinant from the pivots when the square matrix is reduced to upper triangular form using Gaussian elimination. However, originally the determinant was defined through cofactor expansion.

A minor of a matrix $${\bf A} = [a_{i,j} ]$$ is the determinant of some smaller square matrix, cut down from A by removing one or more of its rows or columns. The minor of entry ai,j of a square n-by-n matrix A is denoted by Mi,j and the determinant of the $$(n-1) \times (n-1)$$ submatrix that remains after the i-th row and j-th colum are deleted from A. The number $$(-1)^{i+j} M_{i,j}$$ is denoted by $$C_{i,j}$$ and is called the cofactor of entry ai,j.

The term minor is apparently due to the English mathematician James Sylvester () who used it in 1850 paper .

Example: The span of the empty set $$\varnothing$$ consists of a unique element 0. Therefore, $$\varnothing$$ is linearly independent and it is a basis for the trivial vector space consisting of the unique element---zero. Its dimension is zero.

Example: In $$\mathbb{R}^n ,$$ the vectors $$e_1 [1,0,0,\ldots , 0] , \quad e_2 =[0,1,0,\ldots , 0], \quad \ldots , e_n =[0,0,\ldots , 0,1]$$ form a basis for n-dimensional real space, and it is called the standard basis. Its dimension is n.

Example: Let us consider the set of all real $$m \times n$$ matrices, and let $${\bf M}_{i,j}$$ denote the matrix whose only nonzero entry is a 1 in the i-th row and j-th column. Then the set $${\bf M}_{i,j} \ : \ 1 \le i \le m , \ 1 \le j \le n$$ is a basis for the set of all such real matrices. Its dimension is mn.

Example: The set of monomials $$\left\{ 1, x, x^2 , \ldots , x^n \right\}$$ form a basis in the set of all polynomials of degree up to n. It has dimension n+1. ■

Example: The infinite set of monomials $$\left\{ 1, x, x^2 , \ldots , x^n , \ldots \right\}$$ form a basis in the set of all polynomials. ■

Theorem: Let V be a vector space and $$\beta = \left\{ {\bf u}_1 , {\bf u}_2 , \ldots , {\bf u}_n \right\}$$ be a subset of V. Then β is a basis for V if and only if each vector v in V can be uniquely decomposed into a linear combination of vectors in β, that is, can be uniquely expressed in the form

${\bf v} = \alpha_1 {\bf u}_1 + \alpha_2 {\bf u}_2 + \cdots + \alpha_n {\bf u}_n$
for unique scalars $$\alpha_1 , \alpha_2 , \ldots , \alpha_n .$$

If the vectors $$\left\{ {\bf u}_1 , {\bf u}_2 , \ldots , {\bf u}_n \right\}$$ form a basis for a vector space V, then every vector in V can be uniquely expressed in the form

${\bf v} = \alpha_1 {\bf u}_1 + \alpha_2 {\bf u}_2 + \cdots + \alpha_n {\bf u}_n$
for appropriately chosen scalars $$\alpha_1 , \alpha_2 , \ldots , \alpha_n .$$ Therefore, v determines a unqiue n-tuple of scalars $$\left[ \alpha_1 , \alpha_2 , \ldots , \alpha_n \right]$$ and, conversely, each n-tuple of scalars determines a unique vector $${\bf v} \in V$$ by using the entries of the n-tuple as the coefficients of a linear combination of $${\bf u}_1 , {\bf u}_2 , \ldots , {\bf u}_n .$$ This fact suggests that V is like the n-dimensional vector space $$\mathbb{R}^n ,$$ where n is the number of vectors in the basis for V.

Theorem: Let S be a linearly independent subset of a vector space V, and let v be an element of V that is not in S. Then $$S \cup \{ {\bf v} \}$$ is linearly dependent if and only if v belongs to the span of the set S.

Theorem: If a vector space V is generated by a finite set S, then some subset of S is a basis for V

A vector space is called finite-dimensional if it has a basis consisting of a finite
number of elements. The unique number of elements in each basis for V is called
the dimension of V and is denoted by dim(V). A vector space that is not finite-
dimensional is called infinite-dimensional.

The next example demonstrates how Mathematica can determine the basis or set of linearly independent vectors from the given set. Note that basis is not unique and even changing the order of vectors, a software can provide you another set of linearly independent vectors.

Example: Suppose we are given four linearly dependent vectors:  MatrixRank[m = {{1, 2, 0, -3, 1, 0}, {1, 2, 2, -3, 1, 2}, {1, 2, 1, -3, 1, 1}, {3, 6, 1, -9, 4, 3}}] 
Out[1]= 3

Then each of the following scripts determine a subset of linearly independent vectors:

 m[[ Flatten[ Position[#, Except[0, _?NumericQ], 1, 1]& /@ Last @ QRDecomposition @ Transpose @ m ] ]] 
Out[2]= {{1, 2, 0, -3, 1, 0}, {1, 2, 2, -3, 1, 2}, {3, 6, 1, -9, 4, 3}}

or, using subroutine

 MinimalSublist[x_List] := Module[{tm, ntm, ytm, mm = x}, {tm = RowReduce[mm] // Transpose, ntm = MapIndexed[{#1, #2, Total[#1]} &, tm, {1}], ytm = Cases[ntm, {___, ___, d_ /; d == 1}]}; Cases[ytm, {b_, {a_}, c_} :> mm[[All, a]]] // Transpose] 

we apply it to our set of vectors.

 m1 = {{1, 2, 0, -3, 1, 0}, {1, 2, 1, -3, 1, 2}, {1, 2, 0, -3, 2, 1}, {3, 6, 1, -9, 4, 3}}; MinimalSublist[m1] 
Out[3]= {{1, 0, 1}, {1, 1, 1}, {1, 0, 2}, {3, 1, 4}}

In m1 you see 1 row and n columns together,so you can transpose it to see it as column {{1, 1, 1, 3}, {0, 1, 0, 1}, {1, 1, 2, 4}}

One can use also the standard Mathematica command: IndependenceTest. ■