Linear Sum of Two Subspaces in Linear Algebra: Definition and Theorems

Linear Sum of Two Subspaces in Linear Algebra

Linear Sum of two subspaces is a foundational concept in Linear Algebra, allowing a deeper understanding of how subspaces interact within a vector space. Developed through the study of Mathematics, this concept has proven vital in various fields, including engineering, computer science, and data analysis. By studying the Linear Sum, students can better understand dimensional analysis and the relationships between distinct subspaces.

What You Will Learn?

  • <ul>
    <li><strong>Definition: </strong>Linear Sum of two subspaces</li>
    <li><strong>Theorem-1: </strong> Let \(\pmb{(V,+,\cdot)} \) be a vector space over a field \(\pmb{(F,+,\cdot)} \) and \(\pmb{S} \) and \(\pmb{T} \) be two subspaces of \(\pmb{V} \). Then the linear sum \(\pmb{S+T} \) is a subspace of \(\pmb{V} \).
    </li>
    <li><strong>Theorem-2: </strong>Let \(\pmb{(V,+,\cdot)} \) be a vector space over a field \(\pmb{(F,+,\cdot)} \) and \(\pmb{S} \) and \(\pmb{T} \) be two subspaces of \(\pmb{V} \). Then the subspace \(\pmb{S+T} \) is the smallest subspace of \(\pmb{V} \) containing \(\pmb{S} \) and \(\pmb{T} \).</li>

Things to Remember

Before diving into this post, make sure you are familiar with: Basic Definitions and Concepts of
  1. Mapping
  2. Fields
  3. Vector Space
  4. Binary Composition

Introduction

  The Linear Sum of two subspaces concept arose from early explorations of vector spaces in Mathematics. Initially developed to understand complex structures within spaces, the theory of subspaces and their linear combinations paved the way for modern applications. This concept, rooted in abstract mathematics, was essential in progressing topics within Linear Algebra and is applied to solve complex problems in modern fields, including physics, economics, and machine learning. The Linear Sum method provides insights into how two subspaces combine to span a broader, often richer vector space.

Linear Sum of Two Subspaces

  Definition:

  Let \(\pmb{(V,+,\cdot)} \) be a vector space over a field \(\pmb{(F,+,\cdot)} \) and \(\pmb{S} \) and \(\pmb{T} \) be two subspaces of \(\pmb{V} \). Then the linear sum of of the two subspaces \(\pmb{S} \) and \(\pmb{T} \), a subset of \(\pmb{V} \), is defined \(\pmb{\{s+t~:~s\in S~,~t\in T \} } \) and is denoted by \(\pmb{S+T} \).

Theorem-1

  Statement:

  Let \(\pmb{(V,+,\cdot)} \) be a vector space over a field \(\pmb{(F,+,\cdot)} \) and \(\pmb{S} \) and \(\pmb{T} \) be two subspaces of \(\pmb{V} \). Then the linear sum \(\pmb{S+T} \) is a subspace of \(\pmb{V} \).


  Proof:
  Given that \(\pmb{(V,+,\cdot)} \) is a vector space over a field \(\pmb{(F,+,\cdot)} \) and \(\pmb{S} \) and \(\pmb{T} \) are two subspaces of \(\pmb{V} \).
  To prove \(\pmb{S+T} \) is a subspace of \(\pmb{V} \).

  1. To prove \(\pmb{S+T\ne \Phi} \)
    Since \(\pmb{S} \) and \(\pmb{T} \) are subspaces
    \(\implies \pmb{\theta\in S} \) and \(\pmb{\theta\in T} \)
    \(\implies \pmb{\theta+\theta\in S+T} \)
    \(\implies \pmb{\theta\in S+T} \)
  2. To prove \(\pmb{\alpha+\beta\in S+T~\forall~\alpha,\beta\in S+T} \)
    Let \(\pmb{\alpha,\beta\in S+T} \)
    Then \(\pmb{\exists~ s_{1},s_{2}\in S} \) and \(\pmb{t_{1},t_{2}\in T} \) such that \(\pmb{\alpha=s_{1}+t_{1}} \) and \(\pmb{\beta=s_{2}+t_{2}} \)
    Now \(\pmb{\alpha+\beta= (s_{1}+t_{1})+(s_{2}+t_{2})} \)
    \(\implies \pmb{\alpha+\beta= (s_{1}+s_{2})+(t_{1}+t_{2}) } \)
    \(\implies \pmb{\alpha+\beta= s_{3}+t_{3} }\) where \(\pmb{s_{1}+s_{2}=s_{3}\in S}\) and \(\pmb{t_{1}+t_{2}=t_{3}\in T}\) since \(\pmb{S} \) and \(\pmb{T} \) are subspaces.
    \(\implies \pmb{\alpha+\beta\in S+T }\)
  3. To prove \(\pmb{c\cdot\gamma\in S+T~\forall~\gamma\in S+T,~c\in F} \)
    Let \(\pmb{\gamma\in S+T,~c\in F} \)
    Then \(\pmb{\exists~ s_{3}\in S} \) and \(\pmb{t_{3}\in T} \) such that \(\pmb{\gamma=s_{3}+t_{3}} \).
    Now \(\pmb{c\cdot\gamma=c\cdot(s_{3}+t_{3} )} \)
    \(\implies \pmb{c\cdot\gamma=c\cdot s_{3} +c\cdot t_{3}} \)
    \(\implies \pmb{c\cdot\gamma=s_{4} + t_{4}} \) where \(\pmb{c\cdot s_{3}=s_{4}\in S}\) and \(\pmb{c\cdot t_{3}=t_{4}\in T}\) since \(\pmb{S} \) and \(\pmb{T} \) are subspaces.
    \(\implies \pmb{c\cdot\gamma\in S+T }\)

Theorem-2

  Statement:

  Let \(\pmb{(V,+,\cdot)} \) be a vector space over a field \(\pmb{(F,+,\cdot)} \) and \(\pmb{S} \) and \(\pmb{T} \) be two subspaces of \(\pmb{V} \). Then \(\pmb{S+T} \) is the smallest subspace of \(\pmb{V} \) containing \(\pmb{S} \) and \(\pmb{T} \).


  Proof:
  Given that \(\pmb{(V,+,\cdot)} \) is a vector space over a field \(\pmb{(F,+,\cdot)} \) and \(\pmb{S} \) and \(\pmb{T} \) are two subspaces of \(\pmb{V} \).
  To prove \(\pmb{S+T} \) is the smallest subspace of \(\pmb{V} \) containing \(\pmb{S} \) and \(\pmb{T} \).
  Let \(\pmb{K} \) be a subspace of \(\pmb{V} \) containing \(\pmb{S} \) and \(\pmb{T} \).
  To prove \(\pmb{S+T\subseteq K} \)
  Let \(\pmb{\alpha\in S+T} \)
  Then \(\pmb{\exists~ s_{1}\in S} \) and \(\pmb{t_{2}\in T} \) such that \(\pmb{\alpha=s_{1}+t_{1}} \)
  Since \(\pmb{s_{1}\in S} \) and \(\pmb{t_{2}\in T} \) then \(\pmb{s_{1},t_{1} \in K} \) since \(\pmb{S\subseteq K} \) and \(\pmb{T\subseteq K} \).
  \(\implies\pmb{s_{1}+t_{1} \in K} \) since \(\pmb{K} \) is a subspace.
  \(\implies\pmb{\alpha \in K} \)
  Therefore \(\pmb{S+T\subseteq K} \)
  Since \(\pmb{K} \) be any subspace of \(\pmb{V} \) containing \(\pmb{S} \) and \(\pmb{T} \) therefore \(\pmb{S+T} \) is the smallest subspace of \(\pmb{V} \) containing \(\pmb{S} \) and \(\pmb{T} \).

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

  The Linear Sum of two subspaces is a powerful tool in Linear Algebra, extending beyond theory into numerous real-world applications. By understanding this concept, students gain a foundational skill essential for exploring advanced topics in Mathematics and its applications in fields like physics and computer science.

References

  1. Linear Algebra Done Right by Sheldon Axler
  2. Introduction to Linear Algebra by Gilbert Strang
  3. Linear Algebra by Serge Lang

Related Articles

  • Mappings
  • Binary Compositions
  • Vector Space
  • Linear Transformations

FAQs

  1. What is the Linear Sum of two subspaces?
    The Linear Sum of two subspaces is the set of all possible vectors formed by adding a vector from each subspace.
  2. How is Linear Sum different from Direct Sum?
    A Linear Sum is a direct sum only if the intersection of the two subspaces is zero, meaning no shared elements except the zero vector.
  3. Why is the Linear Sum important in Linear Algebra?
    It helps analyze how subspaces interact and span a vector space, useful in various mathematical and real-world applications.
  4. How is Linear Sum applied in machine learning?
    Linear Sum concepts are used in feature engineering and dimensionality reduction, impacting data representation.
  5. Can the Linear Sum be zero?
    No, the Linear Sum of two non-zero subspaces is always at least the dimension of the larger subspace.
  6. How does Linear Sum apply to control systems?
    Engineers use Linear Sum to span possible states, ensuring system flexibility and control within the vector space.
  7. Is Linear Sum used in Quantum Mechanics?
    Yes, Linear Sum helps in combining quantum states, essential for interpreting multi-dimensional quantum spaces.
  8. What is a practical example of Linear Sum in physics?
    In electromagnetism, Linear Sum is used to model the sum of electric fields from different sources.
Scroll to Top