# Glossary

# Glossary#

**adjacency matrix** (§ 7.1)

Matrix whose nonzero entries show the links between nodes in a graph.

**adjoint** (§ 2.2)

Conjugate transpose of a complex matrix.

**advection equation** (§ 12.1)

Archetypical PDE of hyperbolic type, representing transport phenomena.

**algorithm** (§ 1.3)

List of instructions for transforming data into a result.

**Arnoldi iteration** (§ 8.4)

Stable algorithm for finding orthonormal bases of nested Krylov subspaces.

**asymptotic** (§ 2.5)

Relationship indicating that two functions have the same leading behavior in some limit.

**backward error** (§ 1.4)

Change to the input of a problem required to produce the result found by an inexact algorithm.

**backward substitution** (§ 2.3)

Systematic method for solving a linear system with an upper triangular matrix.

**bandwidth** (§ 2.9)

The number of diagonals around the main diagonal that have nonzero elements.

**barycentric formula** (§ 9.2)

Computationally useful expression for the interpolating polynomial as a ratio of rational terms.

**big-O** (§ 2.5)

Relationship indicating that one function is bounded above by a multiple of another in some limit.

**boundary-value problem** (§ 10.1)

A differential equation with which partial information about the solution is given at multiple points on the boundary of the domain.

**cardinal function** (§ 5.1)

Interpolating function that is 1 at one node and 0 at all the others.

**Cholesky factorization** (§ 2.9)

Symmetrized version of LU factorization for SPD matrices.

**collocation** (§ 10.4)

Solution of a differential equation by imposing it approximately at a set of nodes.

**condition number** (§ 1.2)

Ratio of the size of change in the output of a function to the size of change in the input that produced it.

**cubic spline** (§ 5.3)

Piecewise cubic function with two globally continuous derivatives, most often used for interpolation or approximation.

**diagonalizable** (§ 7.2)

Matrix that admits an eigenvalue decomposition. Also known as nondefective.

**differentiation matrix** (§ 10.3)

Matrix mapping a vector of function values to a vector of approximate derivative values.

**Dirichlet condition** (§ 10.1)

Boundary condition specifying the value of the solution.

**dominant eigenvalue** (§ 8.2)

Eigenvalue with the largest modulus (absolute value, in the real case).

**double precision** (§ 1.1)

Typical standard in floating-point representation, using 64 bits to achieve about 16 decimal significant digits of precision.

**eigenvalue** (§ 7.2)

Scalar \(\lambda\) such that \(\mathbf{A}\mathbf{x} = \lambda \mathbf{x}\) for a square matrix \(\mathbf{A}\) and nonzero vector \(\mathbf{x}\).

**eigenvalue decomposition (EVD)** (§ 7.2)

Expression of a square matrix as the product of eigenvector and diagonal eigenvalue matrices.

**eigenvector** (§ 7.2)

Vector for which the action of a matrix is effectively one-dimensional.

**Euler’s method** (§ 6.2)

Prototype of all IVP solution methods, obtained by assuming constant derivatives for the solution over short time intervals.

**evolutionary PDE** (§ 11.1)

A partial differential equation in which one of the independent variables is time or a close analog.

**extrapolation** (§ 5.6)

Use of multiple discretization values to cancel out leading terms in an error expansion.

**finite difference** (§ 5.4)

Linear combination of function values that approximates the value of a derivative of the function at a point.

**finite element method (FEM)** (§ 10.6)

Use of piecewise integration to pose a linear system of equations for the approximate solution of a boundary-value problem.

**fixed point iteration** (§ 4.2)

Repeated application of a function in hopes of converging to a fixed point.

**fixed point problem** (§ 4.2)

Finding a value of a given function where the input and output values are the same; equivalent to rootfinding.

**floating-point numbers** (§ 1.1)

A finite set that substitutes for the real numbers in machine calculations. Denoted by \(\mathbb{F}\).

**flops** (§ 2.5)

Arithmetic operations on floating-point numbers, often counted as a proxy for computer runtime.

**forward substitution** (§ 2.3)

Systematic method for solving a linear system with a lower triangular matrix.

**Frobenius norm** (§ 2.7)

Matrix norm computed by applying the vector 2-norm to a vector interpretation of the matrix.

**Gauss–Newton method** (§ 4.7)

Generalization of Newton’s method for nonlinear least squares.

**Gaussian elimination** (§ 2.4)

Use of row operations to transform a linear system to an equivalent one in triangular form.

**generating polynomials** (§ 6.6)

A pair of polynomials whose coefficients match those of a multistep method for IVPs.

**global error** (§ 6.2)

Error made by an IVP method over the entire time interval of the solution.

**GMRES** (§ 8.5)

Iterative solution of a linear system through stable least-squares solutions on nested Krylov subspaces.

**graph** (§ 7.1)

Representation of a network as a set of nodes and edges.

**hat functions** (§ 5.2)

Cardinal functions for piecewise linear interpolation.

**heat equation** (§ 11.1)

Archetypical parabolic PDE that describes diffusion.

**hermitian** (§ 7.4)

Combination of transpose and elementwise complex conjugation. Also describes a matrix that equals its own hermitian.

**hermitian positive definite (HPD)** (§ 7.4)

Matrix that is hermitian with strictly positive eigenvalues; complex variant of symmetric positive definite.

**identity matrix** (§ 2.2)

Matrix with ones on the diagonal and zeros elsewhere, acting as the multiplicative identity.

**ill-conditioned** (§ 1.2)

Exhibiting a large condition number, indicating high sensitivity of a result to changes in the data.

**implicit** (§ 6.6)

Formula that defines a quantity only indirectly, e.g., as the solution of a nonlinear equation.

**induced matrix norm** (§ 2.7)

Norm computed using the interpretation of a matrix as a linear operator.

**initial-value problem (IVP)** (§ 6.1, § 6.3)

An ordinary differential equation (possibly vector-valued) together with an initial condition.

**inner product** (§ 9.4)

Scalar or dot product of a pair of vectors, or its extension to a pair of functions.

**interpolation** (§ 2.1, § 5.1)

Construction of a function that passes through a given set of data points.

**inverse iteration** (§ 8.3)

Subtraction of a shift followed by matrix inversion, used in power iteration to transform the eigenvalue closest to a target value into a dominant one.

**Jacobian matrix** (§ 4.5)

Matrix of first partial derivatives that defines the linearization of a vector-valued function.

**Kronecker product** (§ 13.3)

Alternative type of matrix multiplication useful for problems on a tensor-product domain.

**Krylov subspace** (§ 8.4)

Vector space generated by powers of a square matrix that is often useful for reducing the dimension of large problems.

**Lagrange formula** (§ 9.1)

Theoretically useful expression for an interpolating polynomial.

**Lanczos iteration** (§ 8.6)

Specialization of the Arnoldi iteration to the case of a hermitian (or real symmetric) matrix.

**Laplace equation** (§ 13.3)

Archetypical elliptic PDE describing a steady state.

**linear convergence** (§ 4.2)

Sequence in which the difference between sequence value and limit asymptotically decreases by a constant factor at each term, making a straight line on a log-linear graph.

**linear least-squares problem** (§ 3.1)

Minimization of the 2-norm of the residual for an overdetermined linear system.

**local truncation error** (§ 6.2, § 6.6)

Discretization error made in one time step of an IVP solution method.

**LU factorization** (§ 2.4)

Factorization of a square matrix into the product of a unit lower triangular matrix and an upper triangular matrix.

**machine epsilon** (§ 1.1)

Distance from 1 to the next-largest floating-point number. Also called unit roundoff or machine precision, though the usages are not consistent across different references.

**matrix condition number** (§ 2.8)

Norm of the matrix times the norm of its inverse, equivalent to the condition number for solving a linear system.

**method of lines** (§ 11.2)

Solution technique for partial differential equations in which each independent variable is discretized separately.

**multistep** (§ 6.6)

Formula using information over more than a single time step to advance the solution.

**Neumann condition** (§ 10.1)

Boundary condition specifying the derivative of the solution.

**Newton’s method** (§ 4.3)

Rootfinding iteration that uses the linearization of the given function in order to define the next root approximation.

**nodes** (§ 5.1)

Values of the independent variable where an interpolant’s values are prescribed.

**nonlinear least-squares problem** (§ 4.7)

Minimization of the 2-norm of the residual of a function that depends nonlinearly on a vector.

**norm** (§ 2.7)

Means of defining the magnitude of a vector or matrix.

**normal** (§ 7.2)

Matrix that has a unitary eigenvalue decomposition.

**normal equations** (§ 3.2)

Square linear system equivalent to the linear least-squares problem.

**numerical integration** (§ 5.6)

Estimation of a definite integral by combining values of the integrand, rather than by finding an antiderivative.

**one-step IVP method** (§ 6.2)

IVP solver that uses information from just one time level to advance to the next.

**ONC matrix** (§ 3.3)

Matrix whose columns are orthonormal vectors.

**order of accuracy** (§ 5.5, § 5.6, § 6.2, § 6.6)

Leading power of the truncation error as a function of a discretization size parameter.

**orthogonal vectors** (§ 3.3)

Nonzero vectors that have an inner product of zero.

**orthogonal matrix** (§ 3.3)

Square ONC matrix, i.e., matrix whose transpose is its inverse.

**orthogonal polynomials** (§ 9.4)

Family of polynomials whose distinct members have an integral inner product equal to zero, as with Legendre and Chebyshev polynomials.

**orthonormal vectors** (§ 3.3)

Vectors that are both mutually orthogonal and all of unit 2-norm.

**outer product** (§ 2.4)

Multiplication of two vectors resulting in a rank-1 matrix.

**overdetermined** (§ 3.1)

Characterized by having more constraints than available degrees of freedom.

**piecewise linear** (§ 5.2)

Function that is linear between each consecutive pair of nodes, but whose slope may jump at the nodes.

**PLU factorization** (§ 2.6)

LU factorization with row pivoting.

**power iteration** (§ 8.2)

Repeated application of a matrix to a vector, followed by normalization, resulting in convergence to an eigenvector for the dominant eigenvalue.

**preconditioning** (§ 8.8)

Use of an approximate inverse to improve the convergence rate of Krylov iterations for a linear system.

**pseudoinverse** (§ 3.2)

Rectangular matrix that maps data to solution in the linear least-squares problem, generalizing the matrix inverse.

**QR factorization** (§ 3.3)

Representation of a matrix as the product of an orthogonal and an upper triangular matrix.

**quadratic convergence** (§ 4.3)

Sequence in which the difference between sequence value and limit asymptotically decreases by a constant times the square of the preceding difference.

**quasi-Newton methods** (§ 4.6)

Rootfinding methods that overcome the issues of Jacobian computation and lack of global convergence in Newton’s method.

**quasimatrix** (§ 9.4)

Collection of functions (such as orthogonal polynomials) that have algebraic parallels to columns of a matrix.

**Rayleigh quotient** (§ 7.4)

Function of vectors that equals an eigenvalue when given its eigenvector as input.

**reduced QR factorization**

See *thin QR*.

**reduced SVD**

See *thin SVD*.

**residual** (§ 2.8, § 4.1)

For a linear system, the difference between \(\mathbf{b}\) and \(\mathbf{A}\tilde{\mathbf{x}}\) for a computed solution approximation \(\tilde{\mathbf{x}}\). More generally, the actual value of a quantity that is made zero by an exact solution.

**restarting** (§ 8.5)

Technique used in GMRES to prevent the work per iteration and overall storage from growing uncontrollably.

**rootfinding problem** (§ 4.1)

Finding the input value for a given function which makes that function zero.

**row pivoting** (§ 2.6)

Reordering rows during LU factorization to ensure that the factorization exists and can be computed stably.

**Runge phenomenon** (§ 9.3)

Manifestation of the instability of polynomial interpolation at equally spaced nodes as degree increases.

**Runge–Kutta** (§ 6.4)

One-step method for IVPs that evaluates the derivative of the solution more than once to advance a single step.

**secant method** (§ 4.4)

Scalar quasi-Newton method that uses a secant line rather than a tangent line to define a root estimate.

**shooting** (§ 10.2)

Unstable technique for solving a boundary-value problem in which an initial value is sought for by a rootfinding algorithm.

**simple root** (§ 4.1)

Root of a function at which the derivative of the function is nonzero.

**singular value decomposition (SVD)** (§ 7.3)

Expression of a matrix as a product of two orthogonal/unitary matrices and a nonnegative diagonal matrix.

**sparse** (§ 2.9, § 8.1)

Describing a matrix that has mostly zero elements for structural reasons.

**spectral convergence** (§ 9.3)

Exponentially rapid decrease in error as the number of interpolation nodes increases, e.g., as observed in Chebyshev polynomial and trigonometric interpolation.

**stability region** (§ 11.3)

Region of the complex plane describing when numerical solution of a linear IVP is bounded as \(t\to\infty\).

**step size** (§ 6.2)

Increment in time between successive solution values in a numerical IVP solver.

**stiff** (§ 6.7, § 11.4)

Describes an IVP in which stability is a greater restriction than accuracy for many solution methods, usually favoring the use of an implicit time stepping method.

**subtractive cancellation** (§ 1.2)

Growth in relative error that occurs when two numbers are added/subtracted to get a result that is much smaller in magnitude than the operands; also called *loss of significance* or *cancellation error*.

**superlinear convergence** (§ 4.4)

Sequence for which the convergence is asymptotically faster than any linear rate.

**symmetric matrix** (§ 2.2)

Square matrix that is equal to its transpose.

**symmetric positive definite (SPD) matrix** (§ 2.9)

Matrix that is symmetric and positive definite, thereby permitting a Cholesky factorization. Correspondingly called hermitian positive definite in the complex case.

**tensor-product domain** (§ 13.1)

A domain that can be parameterized using variables that lay in a logical rectangle or cuboid; i.e., each variable independently varies in an interval.

**thin QR factorization** (§ 3.3)

Variant of the QR factorization that discards information not needed to fully represent the original matrix.

**thin SVD** (§ 7.3)

Variant of the singular value decomposition that discards information not needed to fully represent the original matrix.

**trapezoid formula** (§ 5.6, § 6.6)

Numerical integration method resulting from integration of a piecewise linear interpolant.

**triangular matrix** (§ 2.3)

Matrix that is all zero either above (for lower triangular) or below (for upper triangular) the main diagonal.

**tridiagonal matrix** (§ 2.9)

Matrix with nonzeros only on the main diagonal and the adjacent two diagonals.

**trigonometric interpolation** (§ 9.5)

Interpolation of a periodic function by a linear combination of real or complex trigonometric functions.

**truncation error** (§ 5.5, § 5.6)

Difference between an exact value and an approximation, such as one that truncates an infinite series.

**unit triangular matrix** (§ 2.4)

Triangular matrix that has a 1 in every position on the main diagonal.

**unit vector** (§ 2.7)

A vector whose norm equals 1.

**unitary** (§ 7.2)

Square matrix with complex-valued entries whose columns are orthonormal.

**unstable** (§ 1.4)

Allowing perturbations of the data to have much larger effects on the results than can be explained by the problem’s condition number.

**upper Hessenberg** (§ 8.4)

Describing a matrix that has nonzeros only in the upper triangle and first subdiagonal.

**Vandermonde matrix** (§ 2.1)

Matrix whose columns are formed from elementwise powers of a given vector, important for polynomial interpolation and approximation of data.

**weights** (§ 5.4, § 5.6, § 9.6)

Coefficients in a linear combination of function values in a finite-difference or integration method.

**zero-stable** (§ 6.8)

Boundedness property of multistep methods that is required for convergence.