Linear Algebra and Differential Equations

Linear Algebra and Differential Equations Unit 3 – Vector Spaces

Vector spaces form the foundation of linear algebra, providing a framework for understanding and manipulating mathematical objects. They generalize the concept of vectors beyond physical space, allowing us to work with abstract elements that behave like vectors. Key properties of vector spaces include closure under addition and scalar multiplication, associativity, commutativity, and the existence of zero and inverse elements. These properties enable us to perform operations and analyze relationships between vectors, forming the basis for more advanced concepts in linear algebra.

What's a Vector Space?

  • Algebraic structure consisting of a set of elements called vectors and two operations: vector addition and scalar multiplication
  • Vectors can be added together and multiplied by scalars (real or complex numbers) to produce another vector in the space
  • Must satisfy certain axioms (properties) to qualify as a vector space
  • Generalizes the notion of Euclidean vectors (e.g., 2D or 3D vectors) to higher dimensions and abstract settings
  • Examples include:
    • Rn\mathbb{R}^n: n-dimensional real vector space
    • Cn\mathbb{C}^n: n-dimensional complex vector space
    • Space of polynomials of degree n\leq n
    • Space of continuous functions on an interval

Key Properties and Axioms

  • Closure under vector addition: If u\vec{u} and v\vec{v} are vectors in the space, then u+v\vec{u} + \vec{v} is also a vector in the space
  • Closure under scalar multiplication: If v\vec{v} is a vector and cc is a scalar, then cvc\vec{v} is also a vector in the space
  • Associativity of vector addition: (u+v)+w=u+(v+w)(\vec{u} + \vec{v}) + \vec{w} = \vec{u} + (\vec{v} + \vec{w})
  • Commutativity of vector addition: u+v=v+u\vec{u} + \vec{v} = \vec{v} + \vec{u}
  • Existence of zero vector: There exists a unique vector 0\vec{0} such that v+0=v\vec{v} + \vec{0} = \vec{v} for all vectors v\vec{v}
  • Existence of additive inverse: For every vector v\vec{v}, there exists a unique vector v-\vec{v} such that v+(v)=0\vec{v} + (-\vec{v}) = \vec{0}
  • Distributivity of scalar multiplication over vector addition: c(u+v)=cu+cvc(\vec{u} + \vec{v}) = c\vec{u} + c\vec{v}
  • Distributivity of scalar multiplication over field addition: (c+d)v=cv+dv(c + d)\vec{v} = c\vec{v} + d\vec{v}

Types of Vector Spaces

  • Real vector spaces: Vectors with real number components and scalars from the real number field
    • Examples: R2\mathbb{R}^2 (2D plane), R3\mathbb{R}^3 (3D space)
  • Complex vector spaces: Vectors with complex number components and scalars from the complex number field
    • Example: C3\mathbb{C}^3 (3D space with complex components)
  • Function spaces: Vectors are functions and operations are pointwise
    • Examples: space of continuous functions, space of differentiable functions
  • Polynomial spaces: Vectors are polynomials and operations are polynomial addition and scalar multiplication
    • Example: space of polynomials of degree 3\leq 3
  • Matrix spaces: Vectors are matrices and operations are matrix addition and scalar multiplication
    • Example: space of 2×22 \times 2 real matrices

Subspaces and Span

  • Subspace: A subset of a vector space that is itself a vector space under the same operations
    • Must contain the zero vector and be closed under vector addition and scalar multiplication
    • Examples: lines and planes passing through the origin in R3\mathbb{R}^3
  • Span: The set of all linear combinations of a given set of vectors
    • Linear combination: A sum of scalar multiples of vectors, c1v1+c2v2++cnvnc_1\vec{v}_1 + c_2\vec{v}_2 + \cdots + c_n\vec{v}_n
    • Spanning set: A set of vectors whose span is the entire vector space
    • Examples:
      • Span of {(1,0),(0,1)}\{(1, 0), (0, 1)\} is R2\mathbb{R}^2
      • Span of {1,x,x2}\{1, x, x^2\} is the space of polynomials of degree 2\leq 2

Linear Independence and Dependence

  • Linearly independent: A set of vectors is linearly independent if no vector can be written as a linear combination of the others
    • Equivalently, the only solution to c1v1+c2v2++cnvn=0c_1\vec{v}_1 + c_2\vec{v}_2 + \cdots + c_n\vec{v}_n = \vec{0} is c1=c2==cn=0c_1 = c_2 = \cdots = c_n = 0
    • Example: {(1,0),(0,1)}\{(1, 0), (0, 1)\} is linearly independent in R2\mathbb{R}^2
  • Linearly dependent: A set of vectors is linearly dependent if at least one vector can be written as a linear combination of the others
    • Equivalently, there exists a non-trivial solution to c1v1+c2v2++cnvn=0c_1\vec{v}_1 + c_2\vec{v}_2 + \cdots + c_n\vec{v}_n = \vec{0}
    • Example: {(1,0),(0,1),(1,1)}\{(1, 0), (0, 1), (1, 1)\} is linearly dependent in R2\mathbb{R}^2 since (1,1)=(1,0)+(0,1)(1, 1) = (1, 0) + (0, 1)
  • Importance: Linearly independent sets are crucial for defining bases and dimensions of vector spaces

Basis and Dimension

  • Basis: A linearly independent spanning set for a vector space
    • Every vector in the space can be uniquely expressed as a linear combination of basis vectors
    • Examples:
      • Standard basis for R2\mathbb{R}^2: {(1,0),(0,1)}\{(1, 0), (0, 1)\}
      • Standard basis for the space of polynomials of degree 2\leq 2: {1,x,x2}\{1, x, x^2\}
  • Dimension: The number of vectors in a basis for a vector space
    • All bases for a vector space have the same number of vectors
    • Examples:
      • Dimension of Rn\mathbb{R}^n is nn
      • Dimension of the space of polynomials of degree n\leq n is n+1n+1
  • Importance: Bases and dimensions provide a way to represent and analyze vector spaces in a standardized manner

Coordinate Systems

  • Coordinate system: A way to represent vectors in a vector space using a basis
    • Each vector is identified by a unique tuple of scalars (coordinates) corresponding to the basis vectors
    • Example: In R2\mathbb{R}^2 with standard basis {(1,0),(0,1)}\{(1, 0), (0, 1)\}, the vector (3,4)(3, 4) has coordinates 33 and 44
  • Change of basis: Expressing vectors in terms of a different basis
    • Useful for simplifying computations or highlighting certain properties of vectors
    • Achieved through matrix multiplication: [v]B=P[v]A[\vec{v}]_B = P[\vec{v}]_A, where PP is the change of basis matrix
  • Importance: Coordinate systems allow for the numerical representation and manipulation of vectors in a given basis

Applications in Linear Algebra

  • Systems of linear equations: Vector spaces provide a framework for solving and analyzing systems of linear equations
    • Solutions form a subspace (solution space) of the vector space
    • Basis vectors of the solution space give parametric form of solutions
  • Linear transformations: Functions between vector spaces that preserve vector addition and scalar multiplication
    • Can be represented by matrices in given bases
    • Eigenvalues and eigenvectors of the matrix provide insights into the transformation's behavior
  • Least squares approximation: Finding the best-fitting linear model for a set of data points
    • Minimizes the sum of squared distances between data points and the model
    • Solution involves orthogonal projection onto a subspace spanned by the model's basis functions
  • Principal component analysis (PCA): Technique for dimensionality reduction and data compression
    • Identifies the directions (principal components) of maximum variance in the data
    • Projects data onto a lower-dimensional subspace spanned by the principal components


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© 2024 Fiveable Inc. All rights reserved.
AP® and SAT® are trademarks registered by the College Board, which is not affiliated with, and does not endorse this website.