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Square matrix without an inverse
A singular matrix is a square matrix that is not invertible, unlike non-singular matrices which are invertible. Equivalently, an n {\displaystyle n} -by-
Singular_matrix
Matrix decomposition
In linear algebra, the singular value decomposition (SVD) is a factorization of a real or complex matrix into a rotation, followed by a scaling, followed
Singular_value_decomposition
Matrix with a multiplicative inverse
algebra, an invertible matrix (non-singular, non-degenerate or regular) is a square matrix that has an inverse. In other words, if a matrix is invertible, it
Invertible_matrix
Norm on a vector space of matrices
norms. The singular value decomposition is useful in analyzing matrices. A vector norm of the singular values of a matrix may be taken as a matrix norm. Such
Matrix_norm
Square roots of the eigenvalues of the self-adjoint operator
smallest singular value of a matrix A {\displaystyle A} is σ n ( A ) {\displaystyle \sigma _{\mathrm {n} }(A)} . For a non-singular matrix A {\displaystyle
Singular_value
Most widely known generalized inverse of a matrix
numbers. Given a rectangular matrix with real or complex entries, its pseudoinverse is unique. It can be computed using the singular value decomposition. In
Moore–Penrose_inverse
Notion in statistics
some initial results by Francis Ysidro Edgeworth). The Fisher information matrix is used to calculate the covariance matrices associated with maximum-likelihood
Fisher_information
Representation of a matrix as a product
algebra, a matrix decomposition or matrix factorization is a factorization of a matrix into a product of matrices. There are many different matrix decompositions;
Matrix_decomposition
Matrix consisting of linearly independent solutions to a linear differential equation
}}(t)=A(t)\Psi (t)} and Ψ ( t ) {\displaystyle \Psi (t)} is a non-singular matrix for all t {\displaystyle t} . Moreover, if the entries of A ( t ) {\displaystyle
Fundamental matrix (linear differential equation)
Fundamental_matrix_(linear_differential_equation)
Matrix that commutes with its conjugate transpose
matrix whose diagonal values are in general complex and U {\displaystyle U} is a unitary matrix. The left and right singular vectors in the singular value
Normal_matrix
Matrix equal to its conjugate-transpose
mathematics, more precisely in linear algebra, a Hermitian matrix (or self-adjoint matrix) is a square matrix that is equal to its own conjugate transpose—that
Hermitian_matrix
Matrix equal to its transpose
Every real non-singular matrix can be uniquely factored as the product of an orthogonal matrix and a symmetric positive definite matrix, which is called
Symmetric_matrix
Matrix decomposition
List of matrices Matrix decomposition Singular value decomposition Sylvester's formula Golub, Gene H.; Van Loan, Charles F. (1996), Matrix Computations (3rd ed
Eigendecomposition of a matrix
Eigendecomposition_of_a_matrix
Topics referred to by the same term
Singular: Act II, a 2019 studio album by Sabrina Carpenter Singular homology SINGULAR, an open source Computer Algebra System (CAS) Singular matrix,
Singular
Matrix in mathematics
matrix. For the non-singularity of A, according to the Perron–Frobenius theorem, it must be the case that s > ρ(B). Also, for a non-singular M-matrix
M-matrix
Mathematical operation in linear algebra
columns in the first matrix must be equal to the number of rows in the second matrix. The resulting matrix, known as the matrix product, has the number
Matrix_multiplication
Nonparametric spectral estimation method
interpretation. The name "singular spectrum analysis" relates to the spectrum of eigenvalues in a singular value decomposition of a covariance matrix, and not directly
Singular_spectrum_analysis
Set of a matrix's eigenvalues
From this point of view, we can define the pseudo-determinant for a singular matrix to be the product of its nonzero eigenvalues (the density of multivariate
Spectrum_of_a_matrix
Matrix representing a Euclidean rotation
rotation matrix is a transformation matrix that is used to perform a rotation in Euclidean space. For example, using the convention below, the matrix R = [
Rotation_matrix
Type of matrix representation
{\displaystyle P} have determinant 1. The positive-semidefinite matrix P is always unique, even if A is singular, and can be obtained as P = ( A ∗ A ) 1 / 2 , {\displaystyle
Polar_decomposition
Real square matrix whose columns and rows are orthogonal unit vectors
In linear algebra, an orthogonal matrix or orthonormal matrix Q, is a real-valued square matrix whose columns and rows are orthonormal vectors. One way
Orthogonal_matrix
Concept in linear algebra
P ) ≠ K {\displaystyle \lambda (P)\neq K} ; otherwise it is called singular. Matrix pencils play an important role in numerical linear algebra. The problem
Matrix_pencil
Idempotent linear transformation from a vector space to itself
is a non-singular matrix and A T B = 0 {\displaystyle A^{\mathsf {T}}B=0} (i.e., B {\displaystyle B} is the null space matrix of A {\displaystyle
Projection_(linear_algebra)
Matrix that converges to zero matrix
successive powers), the matrix T converges to the zero matrix. A regular splitting of a non-singular matrix A results in a convergent matrix T. A semi-convergent
Convergent_matrix
Maltese mathematician
her research has been the singular graphs, graphs whose adjacency matrix is a singular matrix, and the nut graphs, singular graphs all of whose nontrivial
Irene_Sciriha
Filling in missing entries of a matrix
i^{\text{th}}} right singular vector of M {\displaystyle M} , v i {\displaystyle v_{i}} , can be changed to some arbitrary value and still yield a matrix matching
Matrix_completion
Theorem of matrix ranks
of the inverse of the matrix A + B where the matrix B can be approximated by a low-rank matrix UCV, for example using the singular value decomposition.
Woodbury_matrix_identity
Matrix of second derivatives
In mathematics, the Hessian matrix, Hessian or (less commonly) Hesse matrix is a square matrix of second-order partial derivatives of a scalar-valued function
Hessian_matrix
Dimension of the column space of a matrix
when a value, such as a singular value from the SVD, should be treated as zero, a practical choice which depends on both the matrix and the application.
Rank_(linear_algebra)
Mathematical concept in algebra
t} . Every singular matrix can be written as a product of nilpotent matrices. A nilpotent matrix is a special case of a convergent matrix. A linear operator
Nilpotent_matrix
Square matrix in which each ascending skew-diagonal from left to right is constant
In linear algebra, a Hankel matrix (or catalecticant matrix), named after Hermann Hankel, is a rectangular matrix in which each ascending skew-diagonal
Hankel_matrix
Branch of mathematics
(M-aI)z=0.} As z is supposed to be nonzero, this means that M – aI is a singular matrix, and thus that its determinant det (M − aI) equals zero. The eigenvalues
Linear_algebra
Name of two different techniques based on the singular value decomposition
on the left and right singular vectors of a single-matrix SVD. The generalized singular value decomposition (GSVD) is a matrix decomposition on a pair
Generalized singular value decomposition
Generalized_singular_value_decomposition
Arithmetical operation
associativity, and inclusion of identity (the identity matrix) and inverses. However, matrix multiplication is not commutative, which shows that this
Multiplication
Matrix representation of a graph
theory, the Laplacian matrix, also called the graph Laplacian, admittance matrix, Kirchhoff matrix, or discrete Laplacian, is a matrix representation of a
Laplacian_matrix
System of equations in mathematics
the Jacobian matrix ∂ F ( x ˙ , x , t ) ∂ x ˙ {\displaystyle {\frac {\partial F({\dot {x}},x,t)}{\partial {\dot {x}}}}} is a singular matrix for a DAE system
Differential-algebraic system of equations
Differential-algebraic_system_of_equations
Several equations of degree 1 to be solved simultaneously
If the system has a non-singular matrix (det(A) ≠ 0) then it is also the only solution. If the system has a singular matrix then there is a solution
System_of_linear_equations
Field of mathematics
problems in numerical linear algebra include obtaining matrix decompositions like the singular value decomposition, the QR factorization, the LU factorization
Numerical_linear_algebra
fact that the rows and columns come from the original matrix (rather than left and right singular vectors): There are methods to calculate it with lower
CUR_matrix_approximation
Subclass of matrices
diagonally dominant matrix is trivially a weakly chained diagonally dominant matrix. Weakly chained diagonally dominant matrices are non-singular and include
Diagonally_dominant_matrix
Type of matrix factorization
factorization factors a matrix as the product of a lower triangular matrix and an upper triangular matrix (see matrix multiplication and matrix decomposition)
LU_decomposition
Array of numbers
infinitely many rows and columns. A square matrix A is called invertible or non-singular if there exists a matrix B such that A B = B A = I n , {\displaystyle
Matrix_(mathematics)
Matrix with no negative elements
nonnegative matrix is nonnegative. The inverse of any non-singular M-matrix [clarification needed] is a non-negative matrix. If the non-singular M-matrix is also
Nonnegative_matrix
Quantum algorithm framework
quantum singular value transformation is the block-encoding. A quantum circuit is a block-encoding of a matrix A if it implements a unitary matrix U such
Quantum singular value transformation
Quantum_singular_value_transformation
In mathematics, a bidiagonal matrix is a banded matrix with non-zero entries along the main diagonal and either the diagonal above or the diagonal below
Bidiagonal_matrix
For a square matrix, the transpose of the cofactor matrix
classical adjoint adj(A) of a square matrix A is the transpose of its cofactor matrix. It is occasionally known as adjunct matrix, or "adjoint", though that normally
Adjugate_matrix
In mathematics, invariant of square matrices
an isomorphism. However, if the determinant is zero, the matrix is referred to as singular, meaning it does not have an inverse. The determinant is completely
Determinant
Matrix whose only nonzero elements are on its main diagonal
In linear algebra, a diagonal matrix is a matrix in which the entries outside the main diagonal are all zero; the term usually refers to square matrices
Diagonal_matrix
Point without a tangent space
special singular points were also called nodes. A node is a singular point where the Hessian matrix is non-singular; this implies that the singular point
Singular point of an algebraic variety
Singular_point_of_an_algebraic_variety
matrices used in mathematics, science and engineering. A matrix (plural matrices, or less commonly matrixes) is a rectangular array of numbers called entries
List_of_named_matrices
Method in electric circuit analysis
line of the matrix equation. This procedure results in a ( N − 1 ) × ( N − 1 ) {\displaystyle (N-1)\times (N-1)} dimensional non-singular matrix equation
Nodal_analysis
Matrix-valued random variable
probability theory and mathematical physics, a random matrix is a matrix-valued random variable—that is, a matrix in which some or all of its entries are sampled
Random_matrix
Function's sensitivity to argument change
matrix. Numerical methods for linear least squares Numerical stability Preconditioner Hilbert matrix Ill-posed problem Singular value Wilson matrix Belsley
Condition_number
Technique in natural language processing
diagonal matrix. This is called a singular value decomposition (SVD): X = U Σ V T {\displaystyle {\begin{matrix}X=U\Sigma V^{T}\end{matrix}}} The matrix products
Latent_semantic_analysis
greatest common divisor matrix (sometimes abbreviated as GCD matrix) is a matrix that may also be referred to as Smith's matrix. The study was initiated
GCD_matrix
Result about when a matrix can be diagonalized
result about when a linear operator or matrix can be diagonalized (that is, represented as a diagonal matrix in some basis). This is extremely useful
Spectral_theorem
Integer matrices with +1 or −1 determinant; invertible over the integers. GL_n(Z)
mathematics, a unimodular matrix M is a square integer matrix having determinant +1 or −1. Equivalently, it is an integer matrix that is invertible over
Unimodular_matrix
Vector satisfying some of the criteria of an eigenvector
algebra, a generalized eigenvector of an n × n {\displaystyle n\times n} matrix A {\displaystyle A} is a vector which satisfies certain criteria which are
Generalized_eigenvector
Representation of a matrix as a sum
matrix splitting. The technique was devised by Richard S. Varga in 1960. We seek to solve the matrix equation where A is a given n × n non-singular matrix
Matrix_splitting
Mathematical operation on matrices
\,j=1,\ldots ,r_{\mathbf {B} }.} Since the rank of a matrix equals the number of nonzero singular values, we find that rank ( A ⊗ B ) = rank A rank
Kronecker_product
Method for approximating eigenvalues
corresponding left singular vectors and the singular values, all exactly. For an arbitrary matrix W {\displaystyle W} , we obtain approximate singular triplets
Rayleigh–Ritz_method
Noncommutative geometric structure
functional as a singular trace since all operators have finite rank. For example, matrix algebras have no non-trivial singular traces and the matrix trace is
Singular_trace
Matrix of inner products of vectors
non-negative. The diagonalization of the Gram matrix is the singular value decomposition. The Gram matrix is symmetric in the case the inner product is real-valued;
Gram_matrix
Matrix of geometric progressions
In linear algebra, a Vandermonde matrix, named after Alexandre-Théophile Vandermonde, is a matrix with the terms of a geometric progression in each row:
Vandermonde_matrix
Tensor decomposition
the case of the compact singular value decomposition of a matrix, where the rows and columns corresponding to vanishing singular values are dropped, it
Higher-order singular value decomposition
Higher-order_singular_value_decomposition
{\displaystyle \gamma _{a'}=S\gamma _{a}S^{-1},} where S is a non-singular matrix. The sets γa′ and γa belong to the same equivalence class. Developed
Clifford_module
Structural support for biological cells
In biology, the extracellular matrix (ECM), also called the intercellular matrix, is a network consisting of extracellular macromolecules and minerals
Extracellular_matrix
Point where a mathematical object behaves irregularly
In mathematics, a singularity is a point at which a given mathematical object is not defined, or a point where the mathematical object ceases to be well-behaved
Singularity_(mathematics)
Matrix that, squared, equals itself
a^{2}+bc=a} is idempotent. The only non-singular idempotent matrix is the identity matrix; that is, if a non-identity matrix is idempotent, its number of independent
Idempotent_matrix
Matrix decomposition
zero matrix and Q {\displaystyle Q} is a unitary matrix. From the properties of the singular value decomposition (SVD) and the determinant of a matrix, we
QR_decomposition
Concept in mathematics
In mathematics, a singular perturbation problem is a problem containing a small parameter that cannot be approximated by setting the parameter value to
Singular_perturbation
Function in mathematics
_{01}}\left(\varphi _{1}^{-1}(P(t))\right)\right)} is always a non-singular matrix (provided that the curve P(t) is not stationary), so v1 and v0 cannot
Connection_(mathematics)
Method of data analysis
data's covariance matrix. Thus, the principal components are often computed by eigendecomposition of the data covariance matrix or singular value decomposition
Principal_component_analysis
Linear algebra concept
semi-orthogonal if and only if its non-zero singular values are all equal to 1. A semi-orthogonal matrix A is semi-unitary (either A†A = I or AA† = I)
Semi-orthogonal_matrix
Concepts from linear algebra
Conversely, suppose a matrix A is diagonalizable. Let P be a non-singular square matrix such that P−1AP is some diagonal matrix D. Left multiplying both
Eigenvalues_and_eigenvectors
Asymmetric encryption algorithm developed by Robert McEliece
k\times k} binary non-singular matrix S {\displaystyle S} . Alice selects a random n × n {\displaystyle n\times n} permutation matrix P {\displaystyle P}
McEliece_cryptosystem
Inequalities in number theory and matrix theory
naturally to perturbation of singular values. This result gives the bound for the perturbation in the singular values of a matrix M {\displaystyle M} due to
Weyl's_inequality
Generalization of additive and multiplicative inverses
gin{bmatrix}17&22&27\\22&29&36\\27&36&45\end{bmatrix}}} which is a singular matrix, and cannot be inverted. Division ring Latin square property Loop (algebra)
Inverse_element
Sum of elements on the main diagonal
Singular trace Specht's theorem Trace identity Trace inequalities von Neumann's trace inequality This is immediate from the definition of the matrix product:
Trace_(linear_algebra)
Matrix with the same number of rows and columns
m\times n} matrix A {\displaystyle A} . A square matrix A {\displaystyle A} is called invertible or non-singular if there exists a matrix B {\displaystyle
Square_matrix
Matrix approximation problem in linear algebra
_{R}\|R-M\|_{F}\quad \mathrm {subject\ to} \quad R^{T}R=I} . To find matrix R {\displaystyle R} , one uses the singular value decomposition (for which the entries of Σ {\displaystyle
Orthogonal_Procrustes_problem
Theorem of matrix algebra of invariance properties under basis transformations
{\displaystyle S} be a symmetric square matrix of order n {\displaystyle n} with real entries. Any non-singular square matrix P {\displaystyle P} of the same
Sylvester's_law_of_inertia
Classification algorithm
with a specified covariance matrix. Suppose X {\displaystyle X} is a random (column) vector with non-singular covariance matrix Σ {\displaystyle \Sigma }
Whitening_transformation
Form of a matrix
linear algebra, a skew-symmetric (or antisymmetric or antimetric) matrix is a square matrix whose transpose equals its negative. That is, it satisfies the
Skew-symmetric_matrix
Algebraic element satisfying some of the criteria of an inverse
them. The purpose of constructing a generalized inverse of a matrix is to obtain a matrix that can serve as an inverse in some sense for a wider class
Generalized_inverse
Wiener process with reflecting spatial boundaries
defined by a d–dimensional drift vector μ a d×d non-singular covariance matrix Σ and a d×d reflection matrix R. where X(t) is an unconstrained Brownian motion
Reflected_Brownian_motion
\min D,T} , are the singular values of W {\displaystyle W} . Models used in multivariate regression are parameterized by a matrix of coefficients. In
Matrix_regularization
Differential equation exhibiting high rate of dissipation
with a singular matrix A {\displaystyle A} , which is common in some applications. (An example is chemical reaction kinetics, where the singularity corresponds
Stiff_equation
Concept in linear algebra
factorization is a matrix decomposition algorithm based on the QR factorization which can be used to determine the rank of a matrix. The singular value decomposition
RRQR_factorization
Example of singular value decomposition (SVD): SimpleSVD s = matA.svd(); SimpleMatrix U = s.getU(); SimpleMatrix W = s.getW(); SimpleMatrix V = s.getV();
Efficient_Java_Matrix_Library
Concept in computer vision
In computer vision, the essential matrix is a 3 × 3 {\displaystyle 3\times 3} matrix, E {\displaystyle \mathbf {E} } that relates corresponding points
Essential_matrix
Coordinate transformation that preserves the form of Hamilton's equations
a non-singular [ ∂ Q i ( q , p ) ∂ p j ] {\textstyle \left[{\frac {\partial Q_{i}(\mathbf {q} ,\mathbf {p} )}{\partial p_{j}}}\right]} matrix whereas
Canonical_transformation
Distribution of singular values of large rectangular random matrices
^{2}} . As the dimensions of a random matrix X {\displaystyle \mathbf {X} } grow larger, the max/min singular values converge to ‖ X ‖ F ( 1 min ( m
Marchenko–Pastur_distribution
Polynomial with all terms of degree two
left by an n × n invertible matrix S, and the symmetric square matrix A is transformed into another symmetric square matrix B of the same size according
Quadratic_form
Mathematical concept
convenience that the base field is the complex numbers. (For a singular fiber with intersection matrix given by an affine Dynkin diagram Γ ~ {\displaystyle {\tilde
Elliptic_surface
Algorithms for matrix decomposition
Non-negative matrix factorization (NMF or NNMF), also non-negative matrix approximation is a group of algorithms in multivariate analysis and linear algebra
Non-negative matrix factorization
Non-negative_matrix_factorization
Computer vision algorithm
algorithm used in computer vision to estimate the essential matrix or the fundamental matrix related to a stereo camera pair from a set of corresponding
Eight-point_algorithm
Generalization of tensor fields
\beta }} is a non-singular matrix and a rank-two tensor density of weight W {\displaystyle W} with covariant indices then its matrix inverse will be a
Tensor_density
Approximation method in statistics
{\boldsymbol {\Sigma }}} is a diagonal matrix of singular values and V {\displaystyle \mathbf {V} } is the orthogonal matrix of the eigenvectors of J T J {\displaystyle
Non-linear_least_squares
Vector operation
In linear algebra, the outer product of two coordinate vectors is the matrix whose entries are all products of an element in the first vector with an element
Outer_product
SINGULAR MATRIX
SINGULAR MATRIX
Girl/Female
Arabic, Muslim
Singular; Unparalleled; Alone; Unique
Girl/Female
Indian
Unique, Singular, Exclusive
Girl/Female
Muslim
Unique, Singular, Exclusive
Girl/Female
Indian
Unique, Singular
Girl/Female
Indian
Unique, Singular, Exclusive
Biblical
lot, singular of Purim (lots, as in Cleromancy [casting of lots])
Girl/Female
Muslim
Unique, Singular, Exclusive
Surname or Lastname
English
English : from Middle English sengler, syngler ‘singular’ (Old French se(i)ngler), perhaps a nickname for a solitary person.German : topographic name for a valley dweller, from a diminutive of Middle High German senke ‘valley’ + the suffix -er, denoting an inhabitant.German : habitational name for someone from Singeln near Waldshut.German : variant of Sing 1.
Girl/Female
Indian
Unique, Singular, Exclusive
Girl/Female
Celtic
Mythical daughter of Lyr.
Girl/Female
Arabic, Muslim
Wish; Desire; Purpose; Use; Aim; Singular of Marib
Girl/Female
Arabic, Muslim
Unique; Singular
Boy/Male
Afghan, Arabic, Danish, French, Kashmiri, Muslim, Pashtun, Sindhi
Singular; Unique; Alone; Exclusively; Unequalled; Exceptional; Peerless
Girl/Female
Arabic, Gujarati, Indian, Kannada, Kashmiri, Muslim, Sindhi
Unique; Singular; Sole; Exclusive
Girl/Female
Muslim
Unique, Singular
Girl/Female
Arabic, Muslim
Present; Gift; Singular of Nihel
Girl/Female
Muslim
Unique, Singular, Exclusive
Girl/Female
Arabic, Muslim
Unique; Singular; Single
Boy/Male
Muslim/Islamic
Singular exclusive, unequalled
Girl/Female
Arabic, Muslim
Present; Gift; Singular of Nihel
SINGULAR MATRIX
SINGULAR MATRIX
Girl/Female
Gujarati, Hindu, Indian
Remover of Fear
Boy/Male
Gujarati, Hindu, Indian, Kannada, Malayalam, Marathi, Mythological, Oriya, Sanskrit, Tamil, Telugu
Sun; Lovely; Name of a Sage; Lord Ganesha
Male
French
French form of Welsh Drystan, probably TRISTAN means "riot, tumult." The change in spelling is due to association with the French word triste, meaning "sad." In Arthurian legend, this was the name of a Knight of the Round Table. He was the son of Blancheflor and Rivalen (Isabelle and Meliodas in later versions), and the nephew of King Mark of Cornwall. He is the hero of the story Tristan and Iseult, in which he is sent to Ireland to fetch Isolde to wed the king but falls in love with her on their return.Â
Boy/Male
Muslim
Masih messiah of the age
Female
German
Pet form of German Sieglinde, SIGI means "gentle battle."Â Compare with masculine Sigi.
Boy/Male
Indian
Better
Girl/Female
Hindu, Indian
Good Day
Boy/Male
Indian
Free of fear
Female
English
Short form of Latin Cleopatra, CLEO means "glory of the father."
Girl/Female
English German
Rose (flower name).
SINGULAR MATRIX
SINGULAR MATRIX
SINGULAR MATRIX
SINGULAR MATRIX
SINGULAR MATRIX
n.
Singular; wonderful; extraordinary.
n.
Any one of numerous species of brachiopod shells belonging to the genus Lingula, and related genera. See Brachiopoda, and Illustration in Appendix.
a.
Of or pertaining to the people of an island; narrow; circumscribed; illiberal; contracted; as, insular habits, opinions, or prejudices.
n.
An individual instance; a particular.
a.
Standing by itself; out of the ordinary course; unusual; uncommon; strange; as, a singular phenomenon.
a.
Distinguished as existing in a very high degree; rarely equaled; eminent; extraordinary; exceptional; as, a man of singular gravity or attainments.
a.
Rather queer; somewhat singular.
n.
Anything singular, rare, or curious.
n.
The singular number, or the number denoting one person or thing; a word in the singular number.
adv.
Strangely; oddly; as, to behave singularly.
adv.
In a singular manner; in a manner, or to a degree, not common to others; extraordinarily; as, to be singularly exact in one's statements; singularly considerate of others.
a.
Each; individual; as, to convey several parcels of land, all and singular.
a.
Fig.: Lean; lank; raw-boned; ungraceful; sharp and stiff in character; as, remarkably angular in his habits and appearance; an angular female.
a.
Denoting one person or thing; as, the singular number; -- opposed to dual and plural.
a.
Being alone; belonging to, or being, that of which there is but one; unique.
n.
See Kickshaws, the correct singular.
a.
Relating to an angle or to angles; having an angle or angles; forming an angle or corner; sharp-cornered; pointed; as, an angular figure.
adv.
So as to express one, or the singular number.
a.
Measured by an angle; as, angular distance.
a.
Of or pertaining to an island; of the nature, or possessing the characteristics, of an island; as, an insular climate, fauna, etc.