Questions and Answers
What is the null space of matrix A?
The null space of a matrix A is the set Nul A of all solutions of the homogeneous equation Ax=0. The null space of an m × n matrix A is a subspace of Rⁿ.
What are the properties of a subspace H in Rⁿ? (Select all that apply)
What is the column space of matrix A?
The column space of a matrix A is the set Col A of all linear combinations of the columns of A.
The set {e₁;...; eη} is called the __ for Rⁿ.
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True or false: The pivot columns of a matrix A form a basis for the column space of A.
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What is the Diagonalization Theorem?
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Define the dimension of a nonzero subspace H.
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True or false: The rank of a matrix A, denoted by rank A, is the dimension of the column space of A.
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What is the rank for matrix A?
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What is the rank theorem?
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What is the basis theorem?
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What are the equivalent statements to the invertible theorem for a matrix A? (Select all that apply)
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What is an eigenvector of a matrix?
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What is called an eigenvalue of A?
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True or false: λ is an eigenvalue of an n × n matrix A if and only if the equation (A - λI)x = 0 has a nontrivial solution.
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True or false: The eigenvalues of a triangular matrix are the entries on its main diagonal.
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What does it mean if the number 0 is not an eigenvalue of A?
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Properties of Determinants: A is invertible if and only if ___
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What is the characteristic equation for an n × n matrix A?
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True or false: If n × n matrices A and B are similar, then they have the same characteristic polynomial and hence the same eigenvalues.
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True or false: An n × n matrix with n distinct eigenvalues is not diagonalizable.
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Let A be an n × n matrix whose distinct eigenvalues are λ₁;...; λₚ. The dimension of the eigenspace for λκ is ______.
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Study Notes
Null Space and Related Theorems
- Null space (Nul A) is the set of all solutions to the homogeneous equation Ax=0.
- Nul A is a subspace of Rⁿ, defined as Nul(A) = {x | Ax=0}, where x is in Rⁿ.
- Null space theorem is important in understanding the solution space of linear equations.
Subspaces
- A subspace H in Rⁿ must contain the zero vector, support closure under addition, and allow scalar multiplication.
- For each u and v in H, u + v must also be in H; for each u in H and scalar c, cu must be in H.
Column Space
- Column space (Col A) consists of all linear combinations of the columns of matrix A.
Standard Basis
- The set {e₁, ..., eη} represents the standard basis for Rⁿ, providing a fundamental set of vectors.
Pivot Columns
- The pivot columns of a matrix A form a basis for its column space, confirming their significance in linear independence.
Diagonalization Theorem
- An nxn matrix A is diagonalizable if it has n linearly independent eigenvectors.
- This can be represented as A = PDP⁻¹, where D is a diagonal matrix containing eigenvalues corresponding to eigenvectors in P.
Dimension of Subspaces
- The dimension of a nonzero subspace H is determined by the number of vectors in any basis for H.
- The dimension of the zero subspace {0} is defined to be zero.
Rank of a Matrix
- The rank of matrix A is the dimension of its column space, denoted as rank A.
- Rank theorem states that for an n-column matrix A, rank A + dim Nul A = n.
Basis Theorem
- A linearly independent set of exactly p elements in a p-dimensional subspace H is a basis for H.
- Similarly, any set of p elements that spans H is also a basis for H.
Invertible Matrix Theorem
- An n × n matrix A is invertible if its columns form a basis of Rⁿ, satisfying various conditions related to rank and null space.
Eigenvectors and Eigenvalues
- An eigenvector of matrix A satisfies the equation Ax = λx for some scalar λ.
- An eigenvalue corresponds to a nontrivial solution of Ax = λx.
Characteristic Equation
- A scalar λ is an eigenvalue of matrix A if it satisfies the equation det(A - λI) = 0.
Properties of Triangular Matrices
- The eigenvalues of a triangular matrix are the entries on its main diagonal.
Determinants
- A matrix A is invertible if and only if det A ≠ 0.
- Properties include det(AB) = (det A)(det B) and det Aⁿ = det A raised to the power of n.
- Row operations affect determinants: row replacement maintains it, row interchange changes the sign, and row scaling multiplies by the scalar.
Similar Matrices
- Similar n × n matrices have the same characteristic polynomial and eigenvalues, ensuring they share essential properties.
Diagonalizability
- An n × n matrix with n distinct eigenvalues is guaranteed to be diagonalizable.
- The dimension of the eigenspace for eigenvalues plays a crucial role in determining diagonalizability.
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Description
Explore key concepts in linear algebra with this quiz. Delve into the null space of matrices and the definition of subspaces in Rⁿ. Perfect for students looking to reinforce their understanding of these fundamental topics.
