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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    Prepare for your Linear Algebra final exam with these essential flashcards. Each card emphasizes key concepts like algebraic multiplicity and basic variables, helping you to solidify your understanding. Ideal for students looking to boost their knowledge before the exam.

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