Linear Algebra Final Exam Flashcards
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Linear Algebra Final Exam Flashcards

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Questions and Answers

What is algebraic multiplicity?

  • The rank of a matrix
  • The multiplicity of an eigenvalue as a root of the characteristic equation (correct)
  • The number of linearly independent eigenvectors
  • The dimension of the column space
  • What defines a basic variable in a linear system?

  • A variable that corresponds to a pivot column in the coefficient matrix (correct)
  • A variable that has a unique solution
  • A variable that is free to take any value
  • The last variable in a row echelon form
  • What is a basis?

    An indexed set B = {v1,...,vP} in V that is linearly independent and spans H.

    What is the best approximation?

    <p>The closest point in a given subspace to a given vector.</p> Signup and view all the answers

    What is the characteristic equation?

    <p>det(A-λI) = 0</p> Signup and view all the answers

    What is the codomain of a transformation T?

    <p>The set R^m that contains the range of T.</p> Signup and view all the answers

    What is the column space?

    <p>The set Col A of all linear combinations of the columns of A.</p> Signup and view all the answers

    A consistent linear system is one with no solutions.

    <p>False</p> Signup and view all the answers

    What does it mean for a matrix to be diagonalizable?

    <p>A matrix that can be written in factored form as PDP⁻¹.</p> Signup and view all the answers

    Define a diagonal matrix.

    <p>A square matrix whose entries not on the main diagonal are all zero.</p> Signup and view all the answers

    What is the dimension of a subspace S?

    <p>The number of vectors in a basis for S.</p> Signup and view all the answers

    What is the domain of a transformation T?

    <p>The set of all vectors x for which T(x) is defined.</p> Signup and view all the answers

    What is an eigenspace?

    <p>The set of all solutions of Ax = λx.</p> Signup and view all the answers

    What is an eigenvalue?

    <p>A scalar λ such that Ax = λx has a solution for some nonzero vector x.</p> Signup and view all the answers

    Define an eigenvector.

    <p>A nonzero vector x such that Ax = λx for some scalar λ.</p> Signup and view all the answers

    What is the Gram-Schmidt process?

    <p>An algorithm for producing an orthogonal or orthonormal basis for a subspace.</p> Signup and view all the answers

    What is a homogeneous equation?

    <p>Ax = 0</p> Signup and view all the answers

    What is the image of a vector x under a transformation T?

    <p>The vector T(x) assigned to x by T.</p> Signup and view all the answers

    An inconsistent system has at least one solution.

    <p>False</p> Signup and view all the answers

    What is an indefinite quadratic form?

    <p>A quadratic form Q such that Q(x) assumes both positive and negative values.</p> Signup and view all the answers

    What does it mean for a vector space to be infinite-dimensional?

    <p>A nonzero vector space V that has no finite basis.</p> Signup and view all the answers

    What is the inverse of a matrix?

    <p>AA⁻¹ = A⁻¹A = I</p> Signup and view all the answers

    Define an invertible linear transformation.

    <p>A linear transformation T: R^N →R^N with an inverse function S.</p> Signup and view all the answers

    What is an invertible matrix?

    <p>A square matrix that possesses an inverse.</p> Signup and view all the answers

    What are isomorphic vector spaces?

    <p>Two vector spaces V and W with a one-to-one linear transformation T mapping V onto W.</p> Signup and view all the answers

    Define isomorphism.

    <p>A one-to-one linear mapping from one vector space to another.</p> Signup and view all the answers

    What is the kernel of a linear transformation?

    <p>The set of x in V such that T(x) = 0.</p> Signup and view all the answers

    What is the least-squares error?

    <p>The distance ‖b - Ax∧‖ from b to Ax∧.</p> Signup and view all the answers

    What is a least-squares solution?

    <p>A vector x∧ such that ‖b - Ax∧‖ ≤ ‖b - Ax‖ for all x in R^n.</p> Signup and view all the answers

    How is length or norm of a vector defined?

    <p>The scalar ‖v‖ = √(v₁² + v₂² + ... + vₙ²).</p> Signup and view all the answers

    What is a linear combination?

    <p>A sum of scalar multiples of vectors.</p> Signup and view all the answers

    A set of vectors is linearly dependent if it has only the trivial solution.

    <p>False</p> Signup and view all the answers

    A set of vectors is linearly independent if the only solution to their linear combination equaling zero is the trivial solution.

    <p>True</p> Signup and view all the answers

    What is a linear transformation T?

    <p>A rule T that assigns a unique vector T(x) in W to each vector x in V, maintaining certain properties.</p> Signup and view all the answers

    What is a negative definite quadratic form?

    <p>A quadratic form Q such that Q(x) &lt; 0 for all x ≠ 0.</p> Signup and view all the answers

    Define a quadratic form.

    <p>A function Q defined for x in R^n by Q(x) = x^(T)Ax.</p> Signup and view all the answers

    What is the range of a linear transformation T?

    <p>The set of all vectors of the form T(x) for some x in the domain of T.</p> Signup and view all the answers

    What is the rank of a matrix A?

    <p>The dimension of the column space of A.</p> Signup and view all the answers

    What does it mean for matrices to be row equivalent?

    <p>Two matrices for which there exists a sequence of row operations that transforms one into the other.</p> Signup and view all the answers

    What is the row space of a matrix A?

    <p>The set Row A of all linear combinations of the rows of A.</p> Signup and view all the answers

    What are similar matrices?

    <p>Matrices A and B such that A = PBP⁻¹ for some invertible matrix P.</p> Signup and view all the answers

    What does Span{v1,...,vP} represent?

    <p>The set of all linear combinations of v1,...,vP.</p> Signup and view all the answers

    Define a symmetric matrix.

    <p>A matrix A such that A^T = A.</p> Signup and view all the answers

    What is a subspace?

    <p>A subset H of some vector space V that contains the zero vector and is closed under addition and scalar multiplication.</p> Signup and view all the answers

    What is the trace of a square matrix A?

    <p>The sum of the diagonal entries in A.</p> Signup and view all the answers

    What is a unique solution?

    <p>The only solution of a system.</p> Signup and view all the answers

    Study Notes

    Key Concepts in Linear Algebra

    • Algebraic Multiplicity: Refers to the number of times an eigenvalue appears as a root of the characteristic equation.

    • Basic Variable: A variable corresponding to a pivot column in the coefficient matrix of a linear system, crucial for finding solutions.

    • Basis: A maximal linearly independent set that spans a subspace H, represented as B = {v1,…,vP}.

    • Best Approximation: The closest point in a subspace to a given vector, key in optimization problems.

    • Characteristic Equation: Defined as det(A-λI) = 0, it is critical in finding eigenvalues of a matrix.

    • Codomain: The set R^m that includes the range of a transformation T; if T maps V to W, then W is the codomain.

    • Column Space: The set Col A comprises all linear combinations of the matrix A's columns, given as Col A = Span {a1,…,aN}.

    • Consistent Linear System: A linear system that has at least one solution, vital for determining solvability.

    • Diagonalizable Matrix: A matrix that can be expressed as PDP⁻¹, where D is a diagonal matrix and P is invertible.

    • Diagonal Matrix: A square matrix with all non-diagonal entries equal to zero, simplifying calculations.

    • Dimension of a Subspace: Indicates the number of vectors in a basis for subspace S, providing insight into its size.

    • Domain of a Transformation: The set of all vectors x for which T(x) is defined, essential in understanding transformer mappings.

    • Eigenspace: Comprises all solutions of Ax = λx for a given eigenvalue λ, including zero vector and all corresponding eigenvectors.

    • Eigenvalue: A scalar λ such that the equation Ax = λx has a solution for some nonzero vector x, fundamental in spectral theory.

    • Eigenvector: A nonzero vector x that satisfies Ax = λx for some scalar λ, representing directions scaled by transformations.

    • Gram-Schmidt Process: An algorithm to create an orthogonal or orthonormal basis from a set of vectors, ensuring minimal redundancy.

    • Homogeneous Equation: Represented as Ax = 0, this form is pivotal in analyzing linear systems.

    • Image of a Vector: The vector T(x) resulting from applying transformation T to vector x, showcasing how transformations operate.

    • Inconsistent System: A linear system with no solutions, indicating contradictions within the system.

    • Indefinite Quadratic Form: A quadratic form that can take both positive and negative values, influencing optimization and geometry.

    • Infinite-Dimensional Vector Space: A vector space V that lacks a finite basis, significant in advanced functional analysis.

    • Inverse of a Matrix: A matrix A possesses an inverse A⁻¹, satisfying AA⁻¹ = A⁻¹A = I, critical in solving linear systems.

    • Invertible Linear Transformation: A transformation T: R^N → R^N where an inverse function S exists, critical for establishing bijuctions.

    • Invertible Matrix: A square matrix that has an inverse, a requisite for many operations in linear algebra.

    • Isomorphic Vector Spaces: Two spaces V and W that can be connected through a one-to-one linear transformation T, preserving structure.

    • Isomorphism: A one-to-one linear mapping between two vector spaces that allows for structural equivalence.

    • Kernel of a Linear Transformation: The set of all vectors in V mapped to the zero vector, essential for understanding dimensions of transformations.

    • Least-Squares Error: The distance ‖b - Ax∧‖ from the vector b to the least-squares solution Ax∧, important in regression analysis.

    • Least-Squares Solution: A vector x∧ that minimizes the residual error from Ax = b, central in approximation methods.

    • Length or Norm of a Vector: Defined as ‖v‖ = √(v^Tv), measuring the magnitude of a vector.

    • Linear Combination: A combination of vectors obtained by scaling and adding them, foundational to understanding vector spaces.

    • Linearly Dependent Set: A set of vectors having a nontrivial solution to the equation c1v1 + ... + cPvP = 0, indicating redundancy.

    • Linearly Independent Set: A set of vectors where the only solution to c1v1 + ... + cPvP = 0 is the trivial solution, indicating uniqueness.

    • Linear Transformation: A function T that maps between vector spaces preserving structure, obeying specific linearity properties.

    • Negative Definite Quadratic Form: A quadratic form that is negative for all nonzero x, indicating a specific curvature in geometric applications.

    • Quadratic Form: Defined by Q(x) = x^(T)Ax, involving symmetric matrices, important in optimization and statistics.

    • Range of a Linear Transformation: Comprises all vectors produced by T(x) for vectors x in its domain, crucial in understanding outputs.

    • Rank of a Matrix: Indicates the dimension of the column space, providing insight into the solutions of a system.

    • Row Equivalent Matrices: Matrices connected by a series of row operations, showing how transformations can yield equivalent systems.

    • Row Space: The set of all linear combinations of the rows of a matrix, similarly characterized as Col A^T.

    • Similar Matrices: Matrices related by A = PBP⁻¹ where P is invertible, important in studying matrix properties.

    • Span: The collection of all linear combinations of a set of vectors, representing the subspace generated by those vectors.

    • Symmetric Matrix: A matrix A where A^T = A, characterized by reflective properties and simplifying many calculations.

    • Subspace: A subset H of vector space V that contains the zero vector and is closed under addition and scalar multiplication.

    • Trace of a Square Matrix: The sum of the diagonal entries, providing insight into the matrix's properties.

    • Unique Solution: The only solution to a system, indicating non-redundancy and specificity in linear systems.

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