Linear Combinations and Linear Span in Linear Algebra: Definition and Key Theorems
Linear Combinations and Linear Span in Linear Algebra
Linear Combinations and Linear Span are foundational concepts in Mathematics and specifically in Linear Algebra. These concepts provide a framework for constructing and understanding vector spaces and subspaces, which are crucial in solving a wide array of mathematical and scientific problems. The theory behind linear combinations and linear spans plays a significant role in the analysis of vector spaces, linear equations, and various applications in physics and engineering.
What You Will Learn?
- Definition: Linear Combination
- Definition: Linear Span
- Theorem-1: Let \(\pmb{(V,+,\cdot)} \) be a vector space over a field \(\pmb{(F,+,\cdot)} \) and \(\pmb{ S} \) be a non empty finite subset of \(\pmb{ V} \). Then the set \(\pmb{L(S) } \) is the smallest subspace of \(\pmb{V} \) containing \(\pmb{ S} \).
- Theorem-2: Let \(\pmb{(V,+,\cdot)} \) be a vector space over a field \(\pmb{(F,+,\cdot)} \). Let \(\pmb{ S} \) and \(\pmb{ T} \) be two non empty finite subset of \(\pmb{ V} \). If \(\pmb{S\subseteq T } \) then \(\pmb{L(S)\subseteq L(T) } \).
- Theorem-3: Let \(\pmb{(V,+,\cdot)} \) be a vector space over a field \(\pmb{(F,+,\cdot)} \). Let \(\pmb{ S} \) and \(\pmb{ T} \) be two non empty finite subset of \(\pmb{ V} \). If \(\pmb{S\subseteq L(T) } \) then \(\pmb{L(S)\subseteq L(T) } \).
- Corollary-1: Let \(\pmb{(V,+,\cdot)} \) be a vector space over a field \(\pmb{(F,+,\cdot)} \). If \(\pmb{ S} \) is a non empty finite subset of \(\pmb{ V} \) then \(\pmb{L(L(S))=L(S) } \).
- Theorem-4: Let \(\pmb{(V,+,\cdot)} \) be a vector space over a field \(\pmb{(F,+,\cdot)} \). If \(\pmb{ S} \) and \(\pmb{ T} \) are two non empty finite subset of \(\pmb{ V} \) then \(\pmb{L(S\cup T)= L(S)+L(T) } \).
- Corollary-2: Let \(\pmb{(V,+,\cdot)} \) be a vector space over a field \(\pmb{(F,+,\cdot)} \). If \(\pmb{ S} \) and \(\pmb{ T} \) are two subspace of \(\pmb{ V} \) then \(\pmb{L(S\cup T)= S+T } \).
Things to Remember
- Mapping
- Fields
- Vector Space
- Subspace
Introduction
Linear Combinations and Linear Span have been pivotal in the development of linear algebra as a mathematical discipline. Linear algebra’s evolution traces back to ancient methods of solving systems of linear equations, with a focus on finding methods to express any vector as a combination of other vectors. The formalized concept of the linear span was introduced to better understand vector spaces, where each vector in a span is represented as a linear sum of two subspaces. This understanding has enabled advancements in physics, engineering, computer graphics, and machine learning.
Linear Combination
Definition:
Let \(\pmb{(V,+,\cdot)} \) be a vector space over a field \(\pmb{(F,+,\cdot)} \) and \(\pmb{\alpha_{1},\alpha_{2},…,\alpha_{n}\in V} \). Then a vector \(\pmb{\beta}\in V \) is said to be a Linear Combination of the vectors \(\pmb{\alpha_{1},\alpha_{2},…,\alpha_{n}} \) if there exists scalars \(\pmb{c_{1},c_{2},…,c_{n} \in F} \) such that \(\pmb{\beta=c_{1}\cdot\alpha_{1}+c_{2}\cdot\alpha_{2}+…+c_{n}\cdot\alpha_{n}} \) i.e., \(\pmb{\beta=\displaystyle\sum_{i=1}^{n} c_{i}\cdot\alpha_{i} } \).
Linear Span
Definition:
Let \(\pmb{(V,+,\cdot)} \) be a vector space over a field \(\pmb{(F,+,\cdot)} \) and \(\pmb{ S} \) be a finite subset of \(\pmb{ V} \). Then the set of all linear combinations of the vectors of \(\pmb{ S} \) is said to be the Linear Span of \(\pmb{ S} \) and is denoted by \(\pmb{L(S)} \).
Note that:
- Let \(\pmb{S=\{\alpha_{1},\alpha_{2},…,\alpha_{n}\} }\) then \(\pmb{L(S)=\left\{\displaystyle\sum_{i=1}^{n} c_{i}\cdot\alpha_{i} :c_{i}\in F \right\} }\)
- Let \(\pmb{S=\Phi }\) then \(\pmb{L(S)=\left\{\theta \right\} }\)
Theorem-1
Statement:
Let \(\pmb{(V,+,\cdot)} \) be a vector space over a field \(\pmb{(F,+,\cdot)} \) and \(\pmb{ S} \) be a non empty finite subset of \(\pmb{ V} \). Then the set \(\pmb{L(S) } \) is the smallest subspace of \(\pmb{V} \) containing \(\pmb{ S} \).
Proof:
Given that \(\pmb{(V,+,\cdot)} \) is a vector space over a field \(\pmb{(F,+,\cdot)} \) and \(\pmb{ S} \) is a non empty finite subset of \(\pmb{ V} \).
To prove \(\pmb{L(S) } \) is the smallest subspace of \(\pmb{V} \) containing \(\pmb{ S} \).
Let \(\pmb{ S=\{\alpha_{1},\alpha_{2},…,\alpha_{n} \} } \).
- To prove \(\pmb{L(S)\ne \Phi} \)
Since \(\pmb{ 0\in F} \) then \(\pmb{\theta=\displaystyle\sum_{i=1}^{n} 0\cdot\alpha_{i}\in L(S)} \). Therefore \(\pmb{L(S)\ne \Phi} \). - To prove \(\pmb{\beta+\gamma\in L(S)~\forall~\beta,\gamma\in L(S)} \)
Let \(\pmb{\beta,\gamma\in L(S)} \)
Then \(\pmb{\exists} \) scalars \(\pmb{c_{i},d_{i}\in F} \) where \(\pmb{i=1,2,…,n} \) such that
\(\pmb{\beta=\displaystyle\sum_{i=1}^{n} c_{i}\cdot\alpha_{i}} \) and \(\pmb{\gamma=\displaystyle\sum_{i=1}^{n} d_{i}\cdot\alpha_{i}} \)
Now \(\pmb{\beta+\gamma=\displaystyle\sum_{i=1}^{n} c_{i}\cdot\alpha_{i}+\displaystyle\sum_{i=1}^{n} d_{i}\cdot\alpha_{i}} \)
\(\implies\pmb{\beta+\gamma=\displaystyle\sum_{i=1}^{n}(c_{i}\cdot\alpha_{i}+d_{i}\cdot\alpha_{i} ) } \)
\(\implies\pmb{\beta+\gamma=\displaystyle\sum_{i=1}^{n}(c_{i}+d_{i} )\cdot\alpha_{i} } \)
\(\implies\pmb{\beta+\gamma\in L(S) } \) since \(\pmb{c_{i}+d_{i}\in F} \) where \(\pmb{i=1,2,…,n} \). - To prove \(\pmb{k\cdot\delta\in L(S)~\forall~\delta\in L(S), k\in F} \)
Let \(\pmb{\delta\in L(S)} \) and \(\pmb{k\in F} \)
Then \(\pmb{\exists} \) scalars \(\pmb{q_{i}\in F} \) where \(\pmb{i=1,2,…,n} \) such that
\(\pmb{\delta=\displaystyle\sum_{i=1}^{n} q_{i}\cdot\alpha_{i}} \)
Now \(\pmb{k\cdot\delta=k\cdot\left(\displaystyle\sum_{i=1}^{n} q_{i}\cdot\alpha_{i} \right) } \)
\(\implies \pmb{k\cdot\delta=\displaystyle\sum_{i=1}^{n} k\cdot\left(q_{i}\cdot\alpha_{i} \right) } \)
\(\implies \pmb{k\cdot\delta=\displaystyle\sum_{i=1}^{n} \left(k\cdot q_{i}\right)\cdot\alpha_{i} } \)
\(\implies\pmb{k\cdot\delta\in L(S) } \) since \(\pmb{k\cdot q_{i}\in F} \) where \(\pmb{i=1,2,…,n} \).
- To prove \(\pmb{L(S) } \) is the smallest subspace of \(\pmb{V} \) containing \(\pmb{ S} \)
Let \(\pmb{K } \) be any subspace of \(\pmb{V} \) containing \(\pmb{ S} \). That is \(\pmb{ S\subseteq K} \).
To prove \(\pmb{L(S)\subseteq K } \)
Let \(\pmb{\eta\in L(S)} \)
Then \(\pmb{\exists} \) scalars \(\pmb{r_{i}\in F} \) where \(\pmb{i=1,2,…,n} \) such that
\(\pmb{\eta=\displaystyle\sum_{i=1}^{n} r_{i}\cdot\alpha_{i}} \)
Since \(\pmb{r_{i}\in F} \) and \(\pmb{\alpha_{i}\in K} \), \(\pmb{i=1,2,…,n} \) then
\(\pmb{r_{i}\cdot \alpha_{i}\in K }\), \(\pmb{i=1,2,…,n} \) since \(\pmb{K } \) is a subspace of \(\pmb{V} \).
\(\implies \pmb{\displaystyle\sum_{i=1}^{n} r_{i}\cdot \alpha_{i}\in K }\) since \(\pmb{K } \) is a subspace of \(\pmb{V} \).
\(\implies \pmb{\eta\in K }\)
Therefore \(\pmb{L(S)\subseteq K } \).
Since \(\pmb{K } \) is any subspace of \(\pmb{V} \) containing \(\pmb{ S} \), hence \(\pmb{L(S) } \) is the smallest subspace of \(\pmb{V} \) containing \(\pmb{ S} \).
Theorem-2
Statement:
Let \(\pmb{(V,+,\cdot)} \) be a vector space over a field \(\pmb{(F,+,\cdot)} \). Let \(\pmb{ S} \) and \(\pmb{ T} \) be two non empty finite subset of \(\pmb{ V} \). If \(\pmb{S\subseteq T } \) then \(\pmb{L(S)\subseteq L(T) } \).
Proof:
Given that \(\pmb{(V,+,\cdot)} \) is a vector space over a field \(\pmb{(F,+,\cdot)} \). \(\pmb{ S} \) and \(\pmb{ T} \) are two non empty finite subset of \(\pmb{ V} \).
Let \(\pmb{S\subseteq T } \).
Let \(\pmb{ S=\{\alpha_{1},\alpha_{2},…,\alpha_{p} \} } \) and \(\pmb{ T=\{\alpha_{1},\alpha_{2},…,\alpha_{p},\beta_{p+1},\beta_{p+2},…,\beta_{q} \} } \)
To prove \(\pmb{L(S)\subseteq L(T) } \)
Let \(\pmb{\gamma\in L(S) } \)
Then \(\pmb{\exists} \) scalars \(\pmb{r_{i}\in F} \) where \(\pmb{i=1,2,…,p} \) such that
\(\pmb{\gamma=\displaystyle\sum_{i=1}^{p} r_{i}\cdot\alpha_{i}} \)
\(\implies \pmb{\gamma=\displaystyle\sum_{i=1}^{p} r_{i}\cdot\alpha_{i}+\displaystyle\sum_{i=p+1}^{q} 0\cdot\beta_{i}} \)
\(\implies \pmb{\gamma \in L(T)} \)
Hence \(\pmb{L(S)\subseteq L(T) } \).
Theorem-3
Statement:
Let \(\pmb{(V,+,\cdot)} \) be a vector space over a field \(\pmb{(F,+,\cdot)} \). Let \(\pmb{ S} \) and \(\pmb{ T} \) be two non empty finite subset of \(\pmb{ V} \). If \(\pmb{S\subseteq L(T) } \) then \(\pmb{L(S)\subseteq L(T) } \).
Proof:
Given that \(\pmb{(V,+,\cdot)} \) is a vector space over a field \(\pmb{(F,+,\cdot)} \). \(\pmb{ S} \) and \(\pmb{ T} \) are two non empty finite subset of \(\pmb{ V} \).
Let \(\pmb{S\subseteq L(T) } \).
Let \(\pmb{ S=\{\alpha_{1},\alpha_{2},…,\alpha_{p} \} } \) and \(\pmb{ T=\{\beta_{1},\beta_{2},…,\beta_{q} \} } \)
Then \(\pmb{\exists} \) scalars \(\pmb{r_{ij}\in F} \) where \(\pmb{i=1,2,…,p} \) and \(\pmb{j=1,2,…,q} \) such that
\(\pmb{\alpha_{i}=\displaystyle\sum_{j=1}^{q} r_{ij}\cdot\beta_{j}} \), \(\pmb{i=1,2,…,p} \).
To prove \(\pmb{L(S)\subseteq L(T) } \)
Let \(\pmb{\gamma\in L(S) } \)
Then \(\pmb{\exists} \) scalars \(\pmb{t_{i}\in F} \) where \(\pmb{i=1,2,…,p} \) such that
\(\pmb{\gamma=\displaystyle\sum_{i=1}^{p} t_{i}\cdot\alpha_{i}} \)
\(\implies \pmb{\gamma=\displaystyle\sum_{i=1}^{p} t_{i}\cdot\left(\displaystyle\sum_{j=1}^{q} r_{ij}\cdot\beta_{j} \right) } \)
\(\implies \pmb{\gamma=\displaystyle\sum_{i=1}^{p}\left[ \displaystyle\sum_{j=1}^{q} t_{i}\cdot\left( r_{ij}\cdot\beta_{j} \right)\right] } \)
\(\implies \pmb{\gamma=\displaystyle\sum_{j=1}^{q}\left[ \displaystyle\sum_{i=1}^{p} \left(t_{i}\cdot r_{ij}\right)\cdot\beta_{j} \right] } \)
\(\implies \pmb{\gamma=\displaystyle\sum_{j=1}^{q}\left[ \displaystyle\sum_{i=1}^{p} \left(t_{i}\cdot r_{ij}\right)\right]\cdot\beta_{j} } \)
\(\implies \pmb{\gamma \in L(T)} \) since \(\pmb{ \displaystyle\sum_{i=1}^{p} \left(t_{i}\cdot r_{ij}\right)\in F }\), \(\pmb{j=1,2,…,q} \)
Hence \(\pmb{L(S)\subseteq L(T) } \).
Corollary-1
Statement:
Let \(\pmb{(V,+,\cdot)} \) be a vector space over a field \(\pmb{(F,+,\cdot)} \). If \(\pmb{ S} \) is a non empty finite subset of \(\pmb{ V} \) then \(\pmb{L(L(S))=L(S) } \).
Proof:
Given that \(\pmb{(V,+,\cdot)} \) is a vector space over a field \(\pmb{(F,+,\cdot)} \).
Let \(\pmb{ S} \) be a non empty finite subset of \(\pmb{ V} \).
To prove \(\pmb{L(L(S))=L(S) } \).
- To prove \(\pmb{L(L(S))\subseteq L(S) } \)
Since \(\pmb{L(S)\subseteq L(S) } \) and \(\pmb{L(S)} \) is a subspace,
then \(\pmb{L(L(S))\subseteq L(S) } \) - To prove \(\pmb{L(S)\subseteq L(L(S)) } \)
Since \(\pmb{S\subseteq L(S) } \) then \(\pmb{L(S)\subseteq L(L(S)) } \)
Therefore \(\pmb{L(L(S))=L(S) } \).
Theorem-4
Statement:
Let \(\pmb{(V,+,\cdot)} \) be a vector space over a field \(\pmb{(F,+,\cdot)} \). If \(\pmb{ S} \) and \(\pmb{ T} \) are two non empty finite subset of \(\pmb{ V} \) then \(\pmb{L(S\cup T)= L(S)+L(T) } \).
Proof:
Given that \(\pmb{(V,+,\cdot)} \) is a vector space over a field \(\pmb{(F,+,\cdot)} \). \(\pmb{ S} \) and \(\pmb{ T} \) are two non empty finite subset of \(\pmb{ V} \).
Let \(\pmb{ S=\{\alpha_{1},\alpha_{2},…,\alpha_{p} \} } \) and \(\pmb{ T=\{\beta_{1},\beta_{2},…,\beta_{q} \} } \)
Then \(\pmb{L(S\cup T)= \{\alpha_{1},\alpha_{2},…,\alpha_{p},\beta_{1},\beta_{2},…,\beta_{q} \} } \).
To prove \(\pmb{L(S\cup T)= L(S)+L(T) } \)
- To prove \(\pmb{L(S\cup T)\subseteq L(S)+L(T) } \)
Let \(\pmb{\gamma \in L(S\cup T)} \)
Then \(\pmb{\exists} \) scalars \(\pmb{r_{i},t_{j}\in F} \) where \(\pmb{i=1,2,…,p} \) and \(\pmb{j=1,2,…,q} \) such that
\(\pmb{\gamma=\displaystyle\sum_{i=1}^{p} r_{i}\cdot\alpha_{i}+\displaystyle\sum_{j=1}^{q} t_{j}\cdot\beta_{j}} \)
Since \(\pmb{\displaystyle\sum_{i=1}^{p} r_{i}\cdot\alpha_{i}\in L(S) } \) and \(\pmb{\displaystyle\sum_{j=1}^{q} t_{j}\cdot\beta_{j}\in L(T) } \), then
\(\pmb{\displaystyle\sum_{i=1}^{p} r_{i}\cdot\alpha_{i}+\displaystyle\sum_{j=1}^{q} t_{j}\cdot\beta_{j}\in L(S)+L(T) } \) - To prove \(\pmb{ L(S)+L(T)\subseteq L(S\cup T) } \)
Since \(\pmb{ S\subseteq S\cup T} \) and \(\pmb{ T\subseteq S\cup T} \)
then \(\pmb{ L(S)\subseteq L(S\cup T) } \) and \(\pmb{ L(T)\subseteq L(S\cup T) } \)
Implies \(\pmb{ L(S\cup T) } \) is a subspace containing \(\pmb{ L(S) } \) and \(\pmb{ L(T) } \).
Since \(\pmb{ L(S)+L(T) } \) is the smallest subspace containing \(\pmb{ L(S) } \) and \(\pmb{ L(T) } \).
Therefore \(\pmb{ L(S)+L(T)\subseteq L(S\cup T) } \).
Hence \(\pmb{L(S\cup T)= L(S)+L(T) } \).
Corollary-2
Statement:
Let \(\pmb{(V,+,\cdot)} \) be a vector space over a field \(\pmb{(F,+,\cdot)} \). If \(\pmb{ S} \) and \(\pmb{ T} \) are two subspaces of \(\pmb{ V} \) then \(\pmb{L(S\cup T)= S+T } \).
Proof:
Given that \(\pmb{(V,+,\cdot)} \) is a vector space over a field \(\pmb{(F,+,\cdot)} \).
Let \(\pmb{ S} \) and \(\pmb{ T} \) be two subspaces of \(\pmb{ V} \).
To prove \(\pmb{L(S\cup T)= S+T } \).
We have \(\pmb{L(S\cup T)= L(S)+L(T) } \).
Since \(\pmb{ S} \) and \(\pmb{ T} \) are two subspaces of \(\pmb{ V} \) then \(\pmb{ L(S)=S} \) and \(\pmb{ L(T)=T} \).
Therefore \(\pmb{L(S\cup T)= L(S)+L(T)=S+T } \).
Applications
- Data Science and Machine Learning
In machine learning, Linear Sum concepts are applied to vector spaces for data representation and feature engineering. - Quantum Mechanics
The combination of subspaces in quantum mechanics is analyzed using Linear Sum to interpret possible outcomes and states. - Control Theory and Signal Processing
Engineers utilize the Linear Sum of subspaces in designing systems, ensuring that the entire vector space of possible states is spanned.
Conclusion
Linear combinations and linear spans have numerous applications in diverse fields:
- Computer Graphics: Used to represent transformations and shapes in 3D modeling.
- Physics: Describes forces, velocities, and other vector quantities in physics.
- Engineering: Essential in circuit analysis and structural mechanics.
- Machine Learning: Used in linear regression, data analysis, and dimensionality reduction techniques.
References
- Linear Algebra Done Right by Sheldon Axler
- Introduction to Linear Algebra by Gilbert Strang
- Linear Algebra by Serge Lang
Related Articles
- Mappings
- Binary Compositions
- Vector Space
- Linear Transformations
FAQs
- What is a linear combination?
A linear combination involves the sum of scalar multiples of vectors. - What does linear span mean?
The linear span of a set of vectors is all possible linear combinations of those vectors. - How is linear span useful in linear algebra?
It helps define the subspace generated by a set of vectors. - What are real-world applications of linear span?
Applications include physics, engineering, and computer graphics. - What is a generating set in linear algebra?
A generating set is a set of vectors whose span covers a vector space. - What is the difference between basis and span?
A basis is a minimal spanning set; a span is the set of all combinations. - How are linear spans related to vector spaces?
Linear spans help identify and define subspaces within vector spaces. - Why are linear combinations important?
They allow for the construction of new vectors within a space. - What role does scalar multiplication play in linear combinations?
Scalar multiplication allows each vector to be scaled before summing. - Can every vector be expressed as a linear combination?
Yes, if the vector is within the span of the given vectors.
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