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Last updated 7:37 AM on 9/3/26
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307 Terms

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The product of a column vector and a row vector resulting in a matrix
Outer Product
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A partitioned matrix with non-zero blocks along the main diagonal and zero matrices elsewhere
Block Diagonal Matrix
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A special matrix that performs an elementary row operation on a target matrix when multiplied on the left
Elementary Matrix
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Gaussian Elimination
An algorithm to solve a system of linear equations in two steps being row reduction and back substitution
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Row Echelon Form
A matrix state where all zero rows are at the bottom and each pivot is to the right of the row above
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Pivot
The first non-zero entry in each row of a matrix in row echelon form
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Upper Triangular Matrix
A square matrix in row echelon form where all elements below the main diagonal are zero
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Elementary Row Operations
The three safe row operations including adding a multiple of one row to another
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Pivot Variable
A variable corresponding to a column containing a pivot whose value can be solved for
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Free Variable
A variable corresponding to a column without a pivot whose value is arbitrary
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Fixed Point Numbers
Numbers with fixed whole number precision and fixed fractional precision that have limited practical use
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Floating Point Numbers
Numbers represented by a sign
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Single Precision
A 32-bit floating point representation available in NumPy as np float32
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Double Precision
A 64-bit floating point representation which is Python and NumPy default
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Round Off Error
Information loss caused in almost every operation due to limited precision arithmetic
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Backward Error Analysis
An approach where the error in a computer computation is pushed back and placed on the data as a perturbed system
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Small Pivots
Values that cause errors to be heavily magnified during division steps in Gaussian elimination
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Partial Pivoting
A strategy where rows are swapped so the pivot always has the largest absolute value in that column
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Complete Pivoting
A strategy using both row and column swaps to place the largest absolute value in the submatrix into the pivot position
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Ill-conditioned System
A system where small data inaccuracies lead to massive deviations in the computed solution
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Flops
Floating point operations used to measure the computational speed and cost of an algorithm
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Flop Count for Row Reduction
The total number of operations required during row reduction scaling at O 2 thirds n cubed
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Flop Count for Back Substitution
The number of operations required during back substitution scaling at O n squared
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Row Vector
A matrix with dimensions 1 by n
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Column Vector
A matrix with dimensions m by 1
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Transpose
An operation that flips a matrix over its diagonal turning rows into columns
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Outer Product
The product of a column vector and a row vector resulting in a matrix
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Block Diagonal Matrix
A partitioned matrix with non-zero blocks along the main diagonal and zero matrices elsewhere
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Elementary Matrix
A special matrix that performs an elementary row operation on a target matrix when multiplied on the left
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What is the span of a set of vectors?
The collection of all linear combinations of those vectors
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What is the column space of matrix A?
The span of the columns of A
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What is the null space of matrix A?
The set of all solutions to the homogeneous matrix equation Ax = 0
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When does Ax = b have a solution?
When vector b belongs to the column space of A
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What makes a set of vectors linearly independent?
The only solution to their linear combination equaling zero is if all coefficients are zero
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What is a basis for a subspace?
A linearly independent spanning set
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What is the dimension of a subspace?
The number of vectors in its basis
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What is the nullity of a matrix?
The dimension of its null space
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What is the rank theorem for an m by n matrix?
Rank(A) plus nullity(A) equals n
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How do you find a basis for the row space?
Find the RREF of A and take the non-zero rows
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How do you find a basis for the column space?
Find the RREF to identify pivot columns and use those original columns from A
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What subspace is orthogonal to the null space?
The row space
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What defines an orthogonal set of vectors?
The dot product of any two distinct vectors in the set is zero
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What defines an orthonormal set?
An orthogonal set where every vector has a length of one
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What is an orthogonal matrix?
A square matrix whose columns form an orthonormal set
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What is the inverse of an orthogonal matrix Q?
Its transpose
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What does an orthogonal transformation preserve?
Both the length of vectors and the angles between them
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What is the purpose of the Gram-Schmidt process?
To convert an arbitrary basis into an orthogonal or orthonormal basis
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How do you find the projection of x onto an orthonormal basis vector q?
Take the dot product of x and q and multiply by q
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What is an idempotent matrix?
A matrix A where A squared equals A
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What two properties define a projection matrix?
It is symmetric and idempotent
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What is the projection matrix P for a unit vector q?
q times q transpose
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What is the projection matrix P for a subspace with orthonormal basis Q?
Q times Q transpose
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What is the null space of a projection matrix P orthogonal to?
The column space of P
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What does QR factorization decompose matrix A into?
An orthogonal matrix Q and an upper triangular matrix R
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Why must the diagonal elements of R in QR factorization be non-zero?
Because the columns of A are linearly independent
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How do you solve Ax = b using QR factorization?
Solve Rx = Q transpose times b using back substitution
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What is the orthogonal complement of a subspace W?
The set of all vectors orthogonal to every vector in W
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What is the orthogonal complement of the column space of A?
The null space of A transpose
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What is the orthogonal complement of the row space of A?
The null space of A
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What does the Orthogonal Decomposition Theorem state?
Any vector v can be uniquely written as w plus w perpendicular
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What happens if you project a vector in the column space of P using P?
The vector remains unchanged
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What form is a linear system rewritten into for fixed point iteration?
x = g(x)
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What matrix splitting is used for iterative linear solvers?
A = P − Q
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What is the general iterative scheme?
Px^(k+1) = Qx^(k) + b
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What makes Jacobi iteration different from Gauss-Seidel?
Jacobi uses only old values
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What does Gauss-Seidel use when computing later components?
Newly computed values
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What type of matrix is P in Jacobi iteration?
Diagonal
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What type of matrix is P in Gauss-Seidel iteration?
Triangular
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What condition is required on the main diagonal for Jacobi and Gauss-Seidel?
No diagonal entries can be zero
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What decomposition splits A into lower diagonal and upper parts?
A = L + D + U
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What is the Jacobi iteration matrix?
M = −D⁻¹(L + U)
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What is the lower triangular Gauss-Seidel iteration matrix?
M = −(L + D)⁻¹U
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What condition guarantees convergence of an iteration in some norm?
||M|| < 1
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What happens to the error during fixed point iteration?
It is multiplied by M
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What matrix property guarantees Jacobi convergence?
Strict diagonal dominance
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What does strict diagonal dominance mean?
Diagonal magnitude exceeds other row magnitudes combined
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Is diagonal dominance necessary for Jacobi convergence?
No it is only sufficient
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Which method generally converges faster?
Gauss-Seidel
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Can rearranging equations help iterative methods converge?
Yes it can create diagonal dominance
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What is a sparse matrix?
A matrix with mostly zero entries
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What is a tridiagonal matrix?
Nonzero entries only on three central diagonals
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Why is Gaussian elimination preferred for tridiagonal systems?
It is very efficient
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What is a vector space closed under?
Linear combinations
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Why must a vector space contain the zero vector?
Scalar multiplication by zero produces it
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Why is the positive quadrant not a vector space?
It is not closed under negative scaling
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What is a subspace?
A vector space contained within another vector space
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Can a subspace equal its parent vector space?
Yes
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What does rank equal in REF or RREF?
The number of pivots
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Does the choice of REF change matrix rank?
No
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What extra conditions define RREF?
Pivots are one with zeros above and below
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What does Ax = b having a solution mean geometrically?
b is in the column space of A
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What is the column space?
All linear combinations of the columns of A
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What does an underdetermined system have?
More columns than rows
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Can an underdetermined system have a unique solution?
No
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What does an overdetermined system have?
More rows than columns
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What can an overdetermined system contain?
Redundant equations or no solution
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For a consistent system what is the number of free variables?
n − rank(A)
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What does each nonpivot column correspond to?
A free variable
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What is a linear combination?
A sum of scalar multiples of vectors
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How can linear combinations be found?
By solving a matrix equation