Basis of a Vector Space
Vector Space
Let's introduce some definitions. A set is called closed with respect to some operation if for any elements of the set the result of applying this operation belongs to the given set. A set of vectors that is closed under linear operations is called a vector space (or linear space).
For example, the set of all vectors parallel to a given plane is a two-dimensional vector space. In fact, if the vectors a, b are parallel to the plane π, then any linear combination of these vectors is also parallel to this plane:
Similarly, the set of all vectors parallel to a given straight line forms a one-dimensional vector space. The zero vector space consists only of the zero vector. A three-dimensional vector space includes the set of all vectors of the space.
More formally, for a set to be a vector space it must satisfy the 8 axioms listed below.
Let V be a non-empty set over a scalar field F. The elements of the set V are vectors a, b, c, ..., and the elements of the field F are numbers λ, μ, ν, ... Two binary operations are defined for the set V over field F:
- Vector addition:
\[\forall \mathbf{a}, \mathbf{b} \in V \;\exists\, \mathbf{c} \in V: \mathbf{a} + \mathbf{b} = \mathbf{c};\]
- Scalar multiplication:
\[\forall \mathbf{a} \in V,\;\forall \lambda \in F \;\exists\, \mathbf{b} \in V: \lambda\mathbf{a} = \mathbf{b}.\]
The set V is a vector space over F, if the following axioms are satisfied:
- \(\mathbf{a} + \mathbf{b} = \mathbf{b} + \mathbf{a}\;\) (commutativity)
- \(\left({\mathbf{a} + \mathbf{b}}\right) + \mathbf{c} = \mathbf{a} + \left({\mathbf{b} + \mathbf{c}}\right)\;\) (associativity)
- \(\mathbf{a} + \mathbf{0} = \mathbf{a}\;\) (existence of identity element)
- \(\mathbf{a} + \left({-\mathbf{a}}\right) = \mathbf{0}\;\) (existence of inverse element)
- \(1 \cdot \mathbf{a} = \mathbf{a}\)
- \(\lambda\left({\mu\mathbf{a}}\right) = \left({\lambda\mu}\right)\mathbf{a}\)
- \(\lambda\left({\mathbf{a} + \mathbf{b}}\right) = \lambda\mathbf{a} + \lambda\mathbf{b}\)
- \(\left({\lambda + \mu}\right)\mathbf{a} = \lambda\mathbf{a} + \mu\mathbf{a}\)
Here a, b, c, 0 are vectors and λ, μ, 1 are numbers.
Examples of Vector Spaces
A key example of a vector space is the set of all free vectors with respect to the usual operations of adding vectors and multiplying a vector by a real number. However, vector space is a rather abstract concept. It can contain not only vectors but also other objects. For example, the following sets are also vector spaces:
- \({M_{n \times m}} -\) the set of matrices of dimension \(n \times m\) with respect to the operation of matrix addition and multiplication by an element of the field of numbers F form a vector space.
- \({P_n\left({x}\right)} -\) the set of polynomials with real coefficients of degree not higher than n form a vector space with respect to the operation of addition of polynomials and multiplication of a polynomial by a real number.
- The set of all functions of the form \(f : \mathbb{R} \to \mathbb{R}\) is a vector space; the addition of vectors and their multiplication by numbers are given by the formulas
\[\left({f + g}\right)\left({x}\right) = f\left({x}\right) + g\left({x}\right),\;\;\left({\alpha \cdot f}\right)\left({x}\right) = \alpha\cdot f\left({x}\right).\]where \(f\left({x}\right), g\left({x}\right)\) are functions, \(\alpha, x\) are real numbers.
- The set of all convergent sequences \(\{u_n\},\) \(\{v_n\}\) with the defined operations of addition and multiplication by a number is a vector space. The sum of sequences and the product of a sequence by a number are given by the formulas
\[\{u_n + v_n\} = \{u_n\} + \{v_n\},\;\;\{\alpha \cdot u_n\} = \alpha \cdot \{u_n\}.\]
From the above examples it can be seen that the elements of a vector space can be of completely different nature: geometric vectors, matrices, polynomials, functions, sequences, and so on.
Basis of a Vector Space
Three linearly independent vectors a, b and c are said to form a basis in space if any vector d can be represented as some linear combination of the vectors a, b and c, that is, if for any vector d there exist real numbers λ, μ, ν such that
This equality is usually called the expansion of the vector d relative to the basis a, b, c and the numbers λ, μ and ν are called the coordinates of the vector d with respect to the basis a, b, c.
Every vector d can be decomposed in a unique way in terms of the basis a, b, c.
A basis for a plane is defined similarly. Two linearly independent vectors a and b lying in a plane π are said to form a basis in this plane if any vector c lying in the plane π can be represented as some linear combination of the vectors a and b, that is, if for any vector c lying in the plane π there exist real numbers λ and μ such that
The following statements are true:
- Any triple of non-coplanar vectors a, b and c form a basis in space.
- Any pair of non-collinear vectors a and b lying in a given plane form a basis in this plane.
Solved Problems
Example 1.
Check that vectors \(\mathbf{a}\left({-3,1}\right)\) and \(\mathbf{b}\left({-1,3}\right)\) form a basis in the plane. Find the coordinates of the vector \(\mathbf{c}\left({-7,5}\right)\) in this basis.
Solution.
Two vectors form a basis in the plane if they are not collinear. Since collinear vectors satisfy the relation \(\mathbf{a} = \lambda\mathbf{b},\) their coordinates must be proportional, that is, the determinant composed of the coordinates of these vectors must be equal to zero. Check it out:
Hence, the vectors \(\mathbf{a}\) and \(\mathbf{b}\) are non-collinear and form a basis. The coordinates of the vector \(\mathbf{c}\left({-7,5}\right)\) in this basis are determined from the equation
Write it in coordinate form
So the vector \(\mathbf{c}\) in basis \(\left({\mathbf{a, b}}\right)\) has the coordinates \(\left({\lambda,\mu}\right) = \left({2,1}\right).\)
Example 2.
Check that vectors \(\mathbf{a}\left({3,0,-1}\right),\) \(\mathbf{b}\left({1,2,-5}\right)\) and \(\mathbf{c}\left({1,0,-1}\right)\) form a basis in space. Find the coordinates of the vector \(\mathbf{d}\left({5,0,-3}\right)\) in this basis.
Solution.
For three vectors \(\mathbf{a},\) \(\mathbf{b}\) and \(\mathbf{c}\) to form a basis in space, they must be non-coplanar. Recall that the conditions for complanarity are
If you make a matrix of three coplanar vectors, its determinant will be equal to zero since the third vector \(\mathbf{c}\) linearly depends on the vectors \(\mathbf{a}\) and \(\mathbf{b}.\) Accordingly, the matrix of three non-coplanar vectors has a non-zero determinant. Let's check what we have in our case:
Therefore, the vectors \(\mathbf{a},\) \(\mathbf{b},\) \(\mathbf{c}\) are linearly independent and form a basis in space. Find the coordinates of the vector \(\mathbf{d}\) in this basis. Write the expansion of this vector in the basis:
Go to the coordinate form:
Therefore, the coordinates of the vector \(\mathbf{d}\) in this basis are \(\left({\lambda,\mu,\nu}\right) = \left({1,0,2}\right).\)
Example 3.
In the parallelogram \(ABCD,\) point \(M\) is the midpoint of the side \(BC\) and point \(O\) is the intersection point of the diagonals. Taking \(\mathbf{AB} = \mathbf{a}\) and \(\mathbf{AD} = \mathbf{b}\) as the basis vectors, find in this basis the coordinates of the vectors \(\mathbf{BD},\) \(\mathbf{CO}\) and \(\mathbf{MD}.\)
Solution.
Express the vector \(\mathbf{BD}\) in terms of vectors \(\mathbf{AB} = \mathbf{a}\) and \(\mathbf{AD} = \mathbf{b}.\) By the vector subtraction rule,
Find now the vector \(\mathbf{CO}.\) Notice that
Since \(\mathbf{CA} = - \mathbf{AC}\) and \(\mathbf{CO} = \frac{1}{2}\mathbf{CA},\) we get
or
Calculate the vector \(\mathbf{MD}:\)
where
Hence
Example 4.
Find a basis for the set of \(2 \times 2\) matrices and determine the coordinates of the matrix
in this basis.
Solution.
As a basis we can take the following matrices
Then an arbitrary \(2 \times 2\) matrix is represented in this basis as follows:
In our case we have
This matrix in the given basis has coordinates \(\left({-1,5,3,0}\right).\)