Mercurial > octave-antonio
diff scripts/special-matrix/gallery.m @ 19833:9fc020886ae9
maint: Clean up m-files to follow Octave coding conventions.
Try to trim long lines to < 80 chars.
Use '##' for single line comments.
Use '(...)' around tests for if/elseif/switch/while.
Abut cell indexing operator '{' next to variable.
Abut array indexing operator '(' next to variable.
Use space between negation operator '!' and following expression.
Use two newlines between endfunction and start of %!test or %!demo code.
Remove unnecessary parens grouping between short-circuit operators.
Remove stray extra spaces (typos) between variables and assignment operators.
Remove stray extra spaces from ends of lines.
| author | Rik <rik@octave.org> |
|---|---|
| date | Mon, 23 Feb 2015 14:54:39 -0800 |
| parents | 4197fc428c7d |
| children | 941e782d0429 |
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--- a/scripts/special-matrix/gallery.m Mon Feb 23 16:42:25 2015 +0000 +++ b/scripts/special-matrix/gallery.m Mon Feb 23 14:54:39 2015 -0800 @@ -505,26 +505,26 @@ function C = cauchy (x, y) ##CAUCHY Cauchy matrix. - ## C = CAUCHY(X, Y), where X, Y are N-vectors, is the N-by-N matrix - ## with C(i,j) = 1/(X(i)+Y(j)). By default, Y = X. - ## Special case: if X is a scalar CAUCHY(X) is the same as CAUCHY(1:X). - ## Explicit formulas are known for DET(C) (which is nonzero if X and Y - ## both have distinct elements) and the elements of INV(C). - ## C is totally positive if 0 < X(1) < ... < X(N) and - ## 0 < Y(1) < ... < Y(N). + ## C = CAUCHY(X, Y), where X, Y are N-vectors, is the N-by-N matrix + ## with C(i,j) = 1/(X(i)+Y(j)). By default, Y = X. + ## Special case: if X is a scalar CAUCHY(X) is the same as CAUCHY(1:X). + ## Explicit formulas are known for DET(C) (which is nonzero if X and Y + ## both have distinct elements) and the elements of INV(C). + ## C is totally positive if 0 < X(1) < ... < X(N) and + ## 0 < Y(1) < ... < Y(N). ## - ## References: - ## N.J. Higham, Accuracy and Stability of Numerical Algorithms, - ## Society for Industrial and Applied Mathematics, Philadelphia, PA, - ## USA, 1996; sec. 26.1. - ## D.E. Knuth, The Art of Computer Programming, Volume 1, - ## Fundamental Algorithms, second edition, Addison-Wesley, Reading, - ## Massachusetts, 1973, p. 36. - ## E.E. Tyrtyshnikov, Cauchy-Toeplitz matrices and some applications, - ## Linear Algebra and Appl., 149 (1991), pp. 1-18. - ## O. Taussky and M. Marcus, Eigenvalues of finite matrices, in - ## Survey of Numerical Analysis, J. Todd, ed., McGraw-Hill, New York, - ## pp. 279-313, 1962. (States the totally positive property on p. 295.) + ## References: + ## N.J. Higham, Accuracy and Stability of Numerical Algorithms, + ## Society for Industrial and Applied Mathematics, Philadelphia, PA, + ## USA, 1996; sec. 26.1. + ## D.E. Knuth, The Art of Computer Programming, Volume 1, + ## Fundamental Algorithms, second edition, Addison-Wesley, Reading, + ## Massachusetts, 1973, p. 36. + ## E.E. Tyrtyshnikov, Cauchy-Toeplitz matrices and some applications, + ## Linear Algebra and Appl., 149 (1991), pp. 1-18. + ## O. Taussky and M. Marcus, Eigenvalues of finite matrices, in + ## Survey of Numerical Analysis, J. Todd, ed., McGraw-Hill, New York, + ## pp. 279-313, 1962. (States the totally positive property on p. 295.) if (nargin < 1 || nargin > 2) error ("gallery: 1 or 2 arguments are required for cauchy matrix."); @@ -561,23 +561,23 @@ function C = chebspec (n, k = 0) ## CHEBSPEC Chebyshev spectral differentiation matrix. - ## C = CHEBSPEC(N, K) is a Chebyshev spectral differentiation - ## matrix of order N. K = 0 (the default) or 1. - ## For K = 0 (`no boundary conditions'), C is nilpotent, with - ## C^N = 0 and it has the null vector ONES(N,1). - ## C is similar to a Jordan block of size N with eigenvalue zero. - ## For K = 1, C is nonsingular and well-conditioned, and its eigenvalues - ## have negative real parts. - ## For both K, the computed eigenvector matrix X from EIG is - ## ill-conditioned (MESH(REAL(X)) is interesting). + ## C = CHEBSPEC(N, K) is a Chebyshev spectral differentiation + ## matrix of order N. K = 0 (the default) or 1. + ## For K = 0 (`no boundary conditions'), C is nilpotent, with + ## C^N = 0 and it has the null vector ONES(N,1). + ## C is similar to a Jordan block of size N with eigenvalue zero. + ## For K = 1, C is nonsingular and well-conditioned, and its eigenvalues + ## have negative real parts. + ## For both K, the computed eigenvector matrix X from EIG is + ## ill-conditioned (MESH(REAL(X)) is interesting). ## - ## References: - ## C. Canuto, M.Y. Hussaini, A. Quarteroni and T.A. Zang, Spectral - ## Methods in Fluid Dynamics, Springer-Verlag, Berlin, 1988; p. 69. - ## L.N. Trefethen and M.R. Trummer, An instability phenomenon in - ## spectral methods, SIAM J. Numer. Anal., 24 (1987), pp. 1008-1023. - ## D. Funaro, Computing the inverse of the Chebyshev collocation - ## derivative, SIAM J. Sci. Stat. Comput., 9 (1988), pp. 1050-1057. + ## References: + ## C. Canuto, M.Y. Hussaini, A. Quarteroni and T.A. Zang, Spectral + ## Methods in Fluid Dynamics, Springer-Verlag, Berlin, 1988; p. 69. + ## L.N. Trefethen and M.R. Trummer, An instability phenomenon in + ## spectral methods, SIAM J. Numer. Anal., 24 (1987), pp. 1008-1023. + ## D. Funaro, Computing the inverse of the Chebyshev collocation + ## derivative, SIAM J. Sci. Stat. Comput., 9 (1988), pp. 1050-1057. if (nargin < 1 || nargin > 2) error ("gallery: 1 to 2 arguments are required for chebspec matrix."); @@ -628,18 +628,18 @@ function C = chebvand (m, p) ## CHEBVAND Vandermonde-like matrix for the Chebyshev polynomials. - ## C = CHEBVAND(P), where P is a vector, produces the (primal) - ## Chebyshev Vandermonde matrix based on the points P, - ## i.e., C(i,j) = T_{i-1}(P(j)), where T_{i-1} is the Chebyshev - ## polynomial of degree i-1. - ## CHEBVAND(M,P) is a rectangular version of CHEBVAND(P) with M rows. - ## Special case: If P is a scalar then P equally spaced points on - ## [0,1] are used. + ## C = CHEBVAND(P), where P is a vector, produces the (primal) + ## Chebyshev Vandermonde matrix based on the points P, + ## i.e., C(i,j) = T_{i-1}(P(j)), where T_{i-1} is the Chebyshev + ## polynomial of degree i-1. + ## CHEBVAND(M,P) is a rectangular version of CHEBVAND(P) with M rows. + ## Special case: If P is a scalar then P equally spaced points on + ## [0,1] are used. ## - ## Reference: - ## N.J. Higham, Stability analysis of algorithms for solving confluent - ## Vandermonde-like systems, SIAM J. Matrix Anal. Appl., 11 (1990), - ## pp. 23-41. + ## Reference: + ## N.J. Higham, Stability analysis of algorithms for solving confluent + ## Vandermonde-like systems, SIAM J. Matrix Anal. Appl., 11 (1990), + ## pp. 23-41. if (nargin < 1 || nargin > 2) error ("gallery: 1 or 2 arguments are required for chebvand matrix."); @@ -680,17 +680,17 @@ function A = chow (n, alpha = 1, delta = 0) ## CHOW Chow matrix - a singular Toeplitz lower Hessenberg matrix. - ## A = CHOW(N, ALPHA, DELTA) is a Toeplitz lower Hessenberg matrix - ## A = H(ALPHA) + DELTA*EYE, where H(i,j) = ALPHA^(i-j+1). - ## H(ALPHA) has p = FLOOR(N/2) zero eigenvalues, the rest being - ## 4*ALPHA*COS( k*PI/(N+2) )^2, k=1:N-p. - ## Defaults: ALPHA = 1, DELTA = 0. + ## A = CHOW(N, ALPHA, DELTA) is a Toeplitz lower Hessenberg matrix + ## A = H(ALPHA) + DELTA*EYE, where H(i,j) = ALPHA^(i-j+1). + ## H(ALPHA) has p = FLOOR(N/2) zero eigenvalues, the rest being + ## 4*ALPHA*COS( k*PI/(N+2) )^2, k=1:N-p. + ## Defaults: ALPHA = 1, DELTA = 0. ## - ## References: - ## T.S. Chow, A class of Hessenberg matrices with known - ## eigenvalues and inverses, SIAM Review, 11 (1969), pp. 391-395. - ## G. Fairweather, On the eigenvalues and eigenvectors of a class of - ## Hessenberg matrices, SIAM Review, 13 (1971), pp. 220-221. + ## References: + ## T.S. Chow, A class of Hessenberg matrices with known + ## eigenvalues and inverses, SIAM Review, 11 (1969), pp. 391-395. + ## G. Fairweather, On the eigenvalues and eigenvectors of a class of + ## Hessenberg matrices, SIAM Review, 13 (1971), pp. 220-221. if (nargin < 1 || nargin > 3) error ("gallery: 1 to 3 arguments are required for chow matrix."); @@ -707,18 +707,18 @@ function C = circul (v) ## CIRCUL Circulant matrix. - ## C = CIRCUL(V) is the circulant matrix whose first row is V. - ## (A circulant matrix has the property that each row is obtained - ## from the previous one by cyclically permuting the entries one step - ## forward; it is a special Toeplitz matrix in which the diagonals - ## `wrap round'.) - ## Special case: if V is a scalar then C = CIRCUL(1:V). - ## The eigensystem of C (N-by-N) is known explicitly. If t is an Nth - ## root of unity, then the inner product of V with W = [1 t t^2 ... t^N] - ## is an eigenvalue of C, and W(N:-1:1) is an eigenvector of C. + ## C = CIRCUL(V) is the circulant matrix whose first row is V. + ## (A circulant matrix has the property that each row is obtained + ## from the previous one by cyclically permuting the entries one step + ## forward; it is a special Toeplitz matrix in which the diagonals + ## `wrap round'.) + ## Special case: if V is a scalar then C = CIRCUL(1:V). + ## The eigensystem of C (N-by-N) is known explicitly. If t is an Nth + ## root of unity, then the inner product of V with W = [1 t t^2 ... t^N] + ## is an eigenvalue of C, and W(N:-1:1) is an eigenvector of C. ## - ## Reference: - ## P.J. Davis, Circulant Matrices, John Wiley, 1977. + ## Reference: + ## P.J. Davis, Circulant Matrices, John Wiley, 1977. if (nargin != 1) error ("gallery: 1 argument is required for circul matrix."); @@ -742,28 +742,28 @@ function A = clement (n, k = 0) ## CLEMENT Clement matrix - tridiagonal with zero diagonal entries. - ## CLEMENT(N, K) is a tridiagonal matrix with zero diagonal entries - ## and known eigenvalues. It is singular if N is odd. About 64 - ## percent of the entries of the inverse are zero. The eigenvalues - ## are plus and minus the numbers N-1, N-3, N-5, ..., (1 or 0). - ## For K = 0 (the default) the matrix is unsymmetric, while for - ## K = 1 it is symmetric. - ## CLEMENT(N, 1) is diagonally similar to CLEMENT(N). + ## CLEMENT(N, K) is a tridiagonal matrix with zero diagonal entries + ## and known eigenvalues. It is singular if N is odd. About 64 + ## percent of the entries of the inverse are zero. The eigenvalues + ## are plus and minus the numbers N-1, N-3, N-5, ..., (1 or 0). + ## For K = 0 (the default) the matrix is unsymmetric, while for + ## K = 1 it is symmetric. + ## CLEMENT(N, 1) is diagonally similar to CLEMENT(N). ## - ## Similar properties hold for TRIDIAG(X,Y,Z) where Y = ZEROS(N,1). - ## The eigenvalues still come in plus/minus pairs but they are not - ## known explicitly. + ## Similar properties hold for TRIDIAG(X,Y,Z) where Y = ZEROS(N,1). + ## The eigenvalues still come in plus/minus pairs but they are not + ## known explicitly. ## - ## References: - ## P.A. Clement, A class of triple-diagonal matrices for test - ## purposes, SIAM Review, 1 (1959), pp. 50-52. - ## A. Edelman and E. Kostlan, The road from Kac's matrix to Kac's - ## random polynomials. In John~G. Lewis, editor, Proceedings of - ## the Fifth SIAM Conference on Applied Linear Algebra Society - ## for Industrial and Applied Mathematics, Philadelphia, 1994, - ## pp. 503-507. - ## O. Taussky and J. Todd, Another look at a matrix of Mark Kac, - ## Linear Algebra and Appl., 150 (1991), pp. 341-360. + ## References: + ## P.A. Clement, A class of triple-diagonal matrices for test + ## purposes, SIAM Review, 1 (1959), pp. 50-52. + ## A. Edelman and E. Kostlan, The road from Kac's matrix to Kac's + ## random polynomials. In John~G. Lewis, editor, Proceedings of + ## the Fifth SIAM Conference on Applied Linear Algebra Society + ## for Industrial and Applied Mathematics, Philadelphia, 1994, + ## pp. 503-507. + ## O. Taussky and J. Todd, Another look at a matrix of Mark Kac, + ## Linear Algebra and Appl., 150 (1991), pp. 341-360. if (nargin < 1 || nargin > 2) error ("gallery: 1 or 2 arguments are required for clement matrix."); @@ -789,17 +789,17 @@ function C = compar (A, k = 0) ## COMP Comparison matrices. - ## COMP(A) is DIAG(B) - TRIL(B,-1) - TRIU(B,1), where B = ABS(A). - ## COMP(A, 1) is A with each diagonal element replaced by its - ## absolute value, and each off-diagonal element replaced by minus - ## the absolute value of the largest element in absolute value in - ## its row. However, if A is triangular COMP(A, 1) is too. - ## COMP(A, 0) is the same as COMP(A). - ## COMP(A) is often denoted by M(A) in the literature. + ## COMP(A) is DIAG(B) - TRIL(B,-1) - TRIU(B,1), where B = ABS(A). + ## COMP(A, 1) is A with each diagonal element replaced by its + ## absolute value, and each off-diagonal element replaced by minus + ## the absolute value of the largest element in absolute value in + ## its row. However, if A is triangular COMP(A, 1) is too. + ## COMP(A, 0) is the same as COMP(A). + ## COMP(A) is often denoted by M(A) in the literature. ## - ## Reference (e.g.): - ## N.J. Higham, A survey of condition number estimation for - ## triangular matrices, SIAM Review, 29 (1987), pp. 575-596. + ## Reference (e.g.): + ## N.J. Higham, A survey of condition number estimation for + ## triangular matrices, SIAM Review, 29 (1987), pp. 575-596. if (nargin < 1 || nargin > 2) error ("gallery: 1 or 2 arguments are required for compar matrix."); @@ -841,26 +841,26 @@ function A = condex (n, k = 4, theta = 100) ## CONDEX `Counterexamples' to matrix condition number estimators. - ## CONDEX(N, K, THETA) is a `counterexample' matrix to a condition - ## estimator. It has order N and scalar parameter THETA (default 100). - ## If N is not equal to the `natural' size of the matrix then - ## the matrix is padded out with an identity matrix to order N. - ## The matrix, its natural size, and the estimator to which it applies - ## are specified by K (default K = 4) as follows: - ## K = 1: 4-by-4, LINPACK (RCOND) - ## K = 2: 3-by-3, LINPACK (RCOND) - ## K = 3: arbitrary, LINPACK (RCOND) (independent of THETA) - ## K = 4: N >= 4, SONEST (Higham 1988) - ## (Note that in practice the K = 4 matrix is not usually a - ## counterexample because of the rounding errors in forming it.) + ## CONDEX(N, K, THETA) is a `counterexample' matrix to a condition + ## estimator. It has order N and scalar parameter THETA (default 100). + ## If N is not equal to the `natural' size of the matrix then + ## the matrix is padded out with an identity matrix to order N. + ## The matrix, its natural size, and the estimator to which it applies + ## are specified by K (default K = 4) as follows: + ## K = 1: 4-by-4, LINPACK (RCOND) + ## K = 2: 3-by-3, LINPACK (RCOND) + ## K = 3: arbitrary, LINPACK (RCOND) (independent of THETA) + ## K = 4: N >= 4, SONEST (Higham 1988) + ## (Note that in practice the K = 4 matrix is not usually a + ## counterexample because of the rounding errors in forming it.) ## - ## References: - ## A.K. Cline and R.K. Rew, A set of counter-examples to three - ## condition number estimators, SIAM J. Sci. Stat. Comput., - ## 4 (1983), pp. 602-611. - ## N.J. Higham, FORTRAN codes for estimating the one-norm of a real or - ## complex matrix, with applications to condition estimation - ## (Algorithm 674), ACM Trans. Math. Soft., 14 (1988), pp. 381-396. + ## References: + ## A.K. Cline and R.K. Rew, A set of counter-examples to three + ## condition number estimators, SIAM J. Sci. Stat. Comput., + ## 4 (1983), pp. 602-611. + ## N.J. Higham, FORTRAN codes for estimating the one-norm of a real or + ## complex matrix, with applications to condition estimation + ## (Algorithm 674), ACM Trans. Math. Soft., 14 (1988), pp. 381-396. if (nargin < 1 || nargin > 3) error ("gallery: 1 to 3 arguments are required for condex matrix."); @@ -913,14 +913,14 @@ function A = cycol (n, k) ## CYCOL Matrix whose columns repeat cyclically. - ## A = CYCOL([M N], K) is an M-by-N matrix of the form A = B(1:M,1:N) - ## where B = [C C C...] and C = RANDN(M, K). Thus A's columns repeat - ## cyclically, and A has rank at most K. K need not divide N. - ## K defaults to ROUND(N/4). - ## CYCOL(N, K), where N is a scalar, is the same as CYCOL([N N], K). + ## A = CYCOL([M N], K) is an M-by-N matrix of the form A = B(1:M,1:N) + ## where B = [C C C...] and C = RANDN(M, K). Thus A's columns repeat + ## cyclically, and A has rank at most K. K need not divide N. + ## K defaults to ROUND(N/4). + ## CYCOL(N, K), where N is a scalar, is the same as CYCOL([N N], K). ## - ## This type of matrix can lead to underflow problems for Gaussian - ## elimination: see NA Digest Volume 89, Issue 3 (January 22, 1989). + ## This type of matrix can lead to underflow problems for Gaussian + ## elimination: see NA Digest Volume 89, Issue 3 (January 22, 1989). if (nargin < 1 || nargin > 2) error ("gallery: 1 or 2 arguments are required for cycol matrix."); @@ -947,19 +947,19 @@ function [c, d, e] = dorr (n, theta = 0.01) ## DORR Dorr matrix - diagonally dominant, ill conditioned, tridiagonal. - ## [C, D, E] = DORR(N, THETA) returns the vectors defining a row diagonally - ## dominant, tridiagonal M-matrix that is ill conditioned for small - ## values of the parameter THETA >= 0. - ## If only one output parameter is supplied then - ## C = FULL(TRIDIAG(C,D,E)), i.e., the matrix iself is returned. - ## The columns of INV(C) vary greatly in norm. THETA defaults to 0.01. - ## The amount of diagonal dominance is given by (ignoring rounding errors): - ## COMP(C)*ONES(N,1) = THETA*(N+1)^2 * [1 0 0 ... 0 1]'. + ## [C, D, E] = DORR(N, THETA) returns the vectors defining a row diagonally + ## dominant, tridiagonal M-matrix that is ill conditioned for small + ## values of the parameter THETA >= 0. + ## If only one output parameter is supplied then + ## C = FULL(TRIDIAG(C,D,E)), i.e., the matrix iself is returned. + ## The columns of INV(C) vary greatly in norm. THETA defaults to 0.01. + ## The amount of diagonal dominance is given by (ignoring rounding errors): + ## COMP(C)*ONES(N,1) = THETA*(N+1)^2 * [1 0 0 ... 0 1]'. ## - ## Reference: - ## F.W. Dorr, An example of ill-conditioning in the numerical - ## solution of singular perturbation problems, Math. Comp., 25 (1971), - ## pp. 271-283. + ## Reference: + ## F.W. Dorr, An example of ill-conditioning in the numerical + ## solution of singular perturbation problems, Math. Comp., 25 (1971), + ## pp. 271-283. if (nargin < 1 || nargin > 2) error ("gallery: 1 or 2 arguments are required for dorr matrix."); @@ -998,27 +998,27 @@ function A = dramadah (n, k = 1) ## DRAMADAH A (0,1) matrix whose inverse has large integer entries. - ## An anti-Hadamard matrix A is a matrix with elements 0 or 1 for - ## which MU(A) := NORM(INV(A),'FRO') is maximal. - ## A = DRAMADAH(N, K) is an N-by-N (0,1) matrix for which MU(A) is - ## relatively large, although not necessarily maximal. - ## Available types (the default is K = 1): - ## K = 1: A is Toeplitz, with ABS(DET(A)) = 1, and MU(A) > c(1.75)^N, - ## where c is a constant. - ## K = 2: A is upper triangular and Toeplitz. - ## The inverses of both types have integer entries. + ## An anti-Hadamard matrix A is a matrix with elements 0 or 1 for + ## which MU(A) := NORM(INV(A),'FRO') is maximal. + ## A = DRAMADAH(N, K) is an N-by-N (0,1) matrix for which MU(A) is + ## relatively large, although not necessarily maximal. + ## Available types (the default is K = 1): + ## K = 1: A is Toeplitz, with ABS(DET(A)) = 1, and MU(A) > c(1.75)^N, + ## where c is a constant. + ## K = 2: A is upper triangular and Toeplitz. + ## The inverses of both types have integer entries. ## - ## Another interesting (0,1) matrix: - ## K = 3: A has maximal determinant among (0,1) lower Hessenberg - ## matrices: det(A) = the n'th Fibonacci number. A is Toeplitz. - ## The eigenvalues have an interesting distribution in the complex - ## plane. + ## Another interesting (0,1) matrix: + ## K = 3: A has maximal determinant among (0,1) lower Hessenberg + ## matrices: det(A) = the n'th Fibonacci number. A is Toeplitz. + ## The eigenvalues have an interesting distribution in the complex + ## plane. ## - ## References: - ## R.L. Graham and N.J.A. Sloane, Anti-Hadamard matrices, - ## Linear Algebra and Appl., 62 (1984), pp. 113-137. - ## L. Ching, The maximum determinant of an nxn lower Hessenberg - ## (0,1) matrix, Linear Algebra and Appl., 183 (1993), pp. 147-153. + ## References: + ## R.L. Graham and N.J.A. Sloane, Anti-Hadamard matrices, + ## Linear Algebra and Appl., 62 (1984), pp. 113-137. + ## L. Ching, The maximum determinant of an nxn lower Hessenberg + ## (0,1) matrix, Linear Algebra and Appl., 183 (1993), pp. 147-153. if (nargin < 1 || nargin > 2) error ("gallery: 1 to 2 arguments are required for dramadah matrix."); @@ -1065,24 +1065,24 @@ function A = fiedler (c) ## FIEDLER Fiedler matrix - symmetric. - ## A = FIEDLER(C), where C is an n-vector, is the n-by-n symmetric - ## matrix with elements ABS(C(i)-C(j)). - ## Special case: if C is a scalar, then A = FIEDLER(1:C) - ## (i.e. A(i,j) = ABS(i-j)). - ## Properties: - ## FIEDLER(N) has a dominant positive eigenvalue and all the other - ## eigenvalues are negative (Szego, 1936). - ## Explicit formulas for INV(A) and DET(A) are given by Todd (1977) - ## and attributed to Fiedler. These indicate that INV(A) is - ## tridiagonal except for nonzero (1,n) and (n,1) elements. - ## [I think these formulas are valid only if the elements of - ## C are in increasing or decreasing order---NJH.] + ## FIEDLER(C), where C is an n-vector, is the n-by-n symmetric + ## matrix with elements ABS(C(i)-C(j)). + ## Special case: if C is a scalar, then A = FIEDLER(1:C) + ## (i.e. A(i,j) = ABS(i-j)). + ## Properties: + ## FIEDLER(N) has a dominant positive eigenvalue and all the other + ## eigenvalues are negative (Szego, 1936). + ## Explicit formulas for INV(A) and DET(A) are given by Todd (1977) + ## and attributed to Fiedler. These indicate that INV(A) is + ## tridiagonal except for nonzero (1,n) and (n,1) elements. + ## [I think these formulas are valid only if the elements of + ## C are in increasing or decreasing order---NJH.] ## - ## References: - ## G. Szego, Solution to problem 3705, Amer. Math. Monthly, - ## 43 (1936), pp. 246-259. - ## J. Todd, Basic Numerical Mathematics, Vol. 2: Numerical Algebra, - ## Birkhauser, Basel, and Academic Press, New York, 1977, p. 159. + ## References: + ## G. Szego, Solution to problem 3705, Amer. Math. Monthly, + ## 43 (1936), pp. 246-259. + ## J. Todd, Basic Numerical Mathematics, Vol. 2: Numerical Algebra, + ## Birkhauser, Basel, and Academic Press, New York, 1977, p. 159. if (nargin != 1) error ("gallery: 1 argument is required for fiedler matrix."); @@ -1107,11 +1107,11 @@ function A = forsythe (n, alpha = sqrt (eps), lambda = 0) ## FORSYTHE Forsythe matrix - a perturbed Jordan block. - ## FORSYTHE(N, ALPHA, LAMBDA) is the N-by-N matrix equal to - ## JORDBLOC(N, LAMBDA) except it has an ALPHA in the (N,1) position. - ## It has the characteristic polynomial - ## DET(A-t*EYE) = (LAMBDA-t)^N - (-1)^N ALPHA. - ## ALPHA defaults to SQRT(EPS) and LAMBDA to 0. + ## FORSYTHE(N, ALPHA, LAMBDA) is the N-by-N matrix equal to + ## JORDBLOC(N, LAMBDA) except it has an ALPHA in the (N,1) position. + ## It has the characteristic polynomial + ## DET(A-t*EYE) = (LAMBDA-t)^N - (-1)^N ALPHA. + ## ALPHA defaults to SQRT(EPS) and LAMBDA to 0. if (nargin < 1 || nargin > 3) error ("gallery: 1 to 3 arguments are required for forsythe matrix."); @@ -1129,41 +1129,41 @@ function F = frank (n, k = 0) ## FRANK Frank matrix---ill conditioned eigenvalues. - ## F = FRANK(N, K) is the Frank matrix of order N. It is upper - ## Hessenberg with determinant 1. K = 0 is the default; if K = 1 the - ## elements are reflected about the anti-diagonal (1,N)--(N,1). - ## F has all positive eigenvalues and they occur in reciprocal pairs - ## (so that 1 is an eigenvalue if N is odd). - ## The eigenvalues of F may be obtained in terms of the zeros of the - ## Hermite polynomials. - ## The FLOOR(N/2) smallest eigenvalues of F are ill conditioned, - ## the more so for bigger N. + ## F = FRANK(N, K) is the Frank matrix of order N. It is upper + ## Hessenberg with determinant 1. K = 0 is the default; if K = 1 the + ## elements are reflected about the anti-diagonal (1,N)--(N,1). + ## F has all positive eigenvalues and they occur in reciprocal pairs + ## (so that 1 is an eigenvalue if N is odd). + ## The eigenvalues of F may be obtained in terms of the zeros of the + ## Hermite polynomials. + ## The FLOOR(N/2) smallest eigenvalues of F are ill conditioned, + ## the more so for bigger N. ## - ## DET(FRANK(N)') comes out far from 1 for large N---see Frank (1958) - ## and Wilkinson (1960) for discussions. + ## DET(FRANK(N)') comes out far from 1 for large N---see Frank (1958) + ## and Wilkinson (1960) for discussions. ## - ## This version incorporates improvements suggested by W. Kahan. + ## This version incorporates improvements suggested by W. Kahan. ## - ## References: - ## W.L. Frank, Computing eigenvalues of complex matrices by determinant - ## evaluation and by methods of Danilewski and Wielandt, J. Soc. - ## Indust. Appl. Math., 6 (1958), pp. 378-392 (see pp. 385, 388). - ## G.H. Golub and J.H. Wilkinson, Ill-conditioned eigensystems and the - ## computation of the Jordan canonical form, SIAM Review, 18 (1976), - ## pp. 578-619 (Section 13). - ## H. Rutishauser, On test matrices, Programmation en Mathematiques - ## Numeriques, Editions Centre Nat. Recherche Sci., Paris, 165, - ## 1966, pp. 349-365. Section 9. - ## J.H. Wilkinson, Error analysis of floating-point computation, - ## Numer. Math., 2 (1960), pp. 319-340 (Section 8). - ## J.H. Wilkinson, The Algebraic Eigenvalue Problem, Oxford University - ## Press, 1965 (pp. 92-93). - ## The next two references give details of the eigensystem, as does - ## Rutishauser (see above). - ## P.J. Eberlein, A note on the matrices denoted by B_n, SIAM J. Appl. - ## Math., 20 (1971), pp. 87-92. - ## J.M. Varah, A generalization of the Frank matrix, SIAM J. Sci. Stat. - ## Comput., 7 (1986), pp. 835-839. + ## References: + ## W.L. Frank, Computing eigenvalues of complex matrices by determinant + ## evaluation and by methods of Danilewski and Wielandt, J. Soc. + ## Indust. Appl. Math., 6 (1958), pp. 378-392 (see pp. 385, 388). + ## G.H. Golub and J.H. Wilkinson, Ill-conditioned eigensystems and the + ## computation of the Jordan canonical form, SIAM Review, 18 (1976), + ## pp. 578-619 (Section 13). + ## H. Rutishauser, On test matrices, Programmation en Mathematiques + ## Numeriques, Editions Centre Nat. Recherche Sci., Paris, 165, + ## 1966, pp. 349-365. Section 9. + ## J.H. Wilkinson, Error analysis of floating-point computation, + ## Numer. Math., 2 (1960), pp. 319-340 (Section 8). + ## J.H. Wilkinson, The Algebraic Eigenvalue Problem, Oxford University + ## Press, 1965 (pp. 92-93). + ## The next two references give details of the eigensystem, as does + ## Rutishauser (see above). + ## P.J. Eberlein, A note on the matrices denoted by B_n, SIAM J. Appl. + ## Math., 20 (1971), pp. 87-92. + ## J.M. Varah, A generalization of the Frank matrix, SIAM J. Sci. Stat. + ## Comput., 7 (1986), pp. 835-839. if (nargin < 1 || nargin > 2) error ("gallery: 1 to 2 arguments are required for frank matrix."); @@ -1196,20 +1196,20 @@ function A = gearmat (n, i = n, j = -n) ## NOTE: this function was named gearm in the original Test Matrix Toolbox ## GEARMAT Gear matrix. - ## A = GEARMAT(N,I,J) is the N-by-N matrix with ones on the sub- and - ## super-diagonals, SIGN(I) in the (1,ABS(I)) position, SIGN(J) - ## in the (N,N+1-ABS(J)) position, and zeros everywhere else. - ## Defaults: I = N, j = -N. - ## All eigenvalues are of the form 2*COS(a) and the eigenvectors - ## are of the form [SIN(w+a), SIN(w+2a), ..., SIN(w+Na)]. - ## The values of a and w are given in the reference below. - ## A can have double and triple eigenvalues and can be defective. - ## GEARMAT(N) is singular. + ## A = GEARMAT(N,I,J) is the N-by-N matrix with ones on the sub- and + ## super-diagonals, SIGN(I) in the (1,ABS(I)) position, SIGN(J) + ## in the (N,N+1-ABS(J)) position, and zeros everywhere else. + ## Defaults: I = N, j = -N. + ## All eigenvalues are of the form 2*COS(a) and the eigenvectors + ## are of the form [SIN(w+a), SIN(w+2a), ..., SIN(w+Na)]. + ## The values of a and w are given in the reference below. + ## A can have double and triple eigenvalues and can be defective. + ## GEARMAT(N) is singular. ## - ## (GEAR is a Simulink function, hence GEARMAT for Gear matrix.) - ## Reference: - ## C.W. Gear, A simple set of test matrices for eigenvalue programs, - ## Math. Comp., 23 (1969), pp. 119-125. + ## (GEAR is a Simulink function, hence GEARMAT for Gear matrix.) + ## Reference: + ## C.W. Gear, A simple set of test matrices for eigenvalue programs, + ## Math. Comp., 23 (1969), pp. 119-125. if (nargin < 1 || nargin > 3) error ("gallery: 1 to 3 arguments are required for gearmat matrix."); @@ -1228,18 +1228,18 @@ function G = grcar (n, k = 3) ## GRCAR Grcar matrix - a Toeplitz matrix with sensitive eigenvalues. - ## GRCAR(N, K) is an N-by-N matrix with -1s on the - ## subdiagonal, 1s on the diagonal, and K superdiagonals of 1s. - ## The default is K = 3. The eigenvalues of this matrix form an - ## interesting pattern in the complex plane (try PS(GRCAR(32))). + ## GRCAR(N, K) is an N-by-N matrix with -1s on the + ## subdiagonal, 1s on the diagonal, and K superdiagonals of 1s. + ## The default is K = 3. The eigenvalues of this matrix form an + ## interesting pattern in the complex plane (try PS(GRCAR(32))). ## - ## References: - ## J.F. Grcar, Operator coefficient methods for linear equations, - ## Report SAND89-8691, Sandia National Laboratories, Albuquerque, - ## New Mexico, 1989 (Appendix 2). - ## N.M. Nachtigal, L. Reichel and L.N. Trefethen, A hybrid GMRES - ## algorithm for nonsymmetric linear systems, SIAM J. Matrix Anal. - ## Appl., 13 (1992), pp. 796-825. + ## References: + ## J.F. Grcar, Operator coefficient methods for linear equations, + ## Report SAND89-8691, Sandia National Laboratories, Albuquerque, + ## New Mexico, 1989 (Appendix 2). + ## N.M. Nachtigal, L. Reichel and L.N. Trefethen, A hybrid GMRES + ## algorithm for nonsymmetric linear systems, SIAM J. Matrix Anal. + ## Appl., 13 (1992), pp. 796-825. if (nargin < 1 || nargin > 2) error ("gallery: 1 to 2 arguments are required for grcar matrix."); @@ -1254,16 +1254,16 @@ function A = hanowa (n, d = -1) ## HANOWA A matrix whose eigenvalues lie on a vertical line in the complex plane. - ## HANOWA(N, d) is the N-by-N block 2x2 matrix (thus N = 2M must be even) - ## [d*EYE(M) -DIAG(1:M) - ## DIAG(1:M) d*EYE(M)] - ## It has complex eigenvalues lambda(k) = d +/- k*i (1 <= k <= M). - ## Parameter d defaults to -1. + ## HANOWA(N, d) is the N-by-N block 2x2 matrix (thus N = 2M must be even) + ## [d*EYE(M) -DIAG(1:M) + ## DIAG(1:M) d*EYE(M)] + ## It has complex eigenvalues lambda(k) = d +/- k*i (1 <= k <= M). + ## Parameter d defaults to -1. ## - ## Reference: - ## E. Hairer, S.P. Norsett and G. Wanner, Solving Ordinary - ## Differential Equations I: Nonstiff Problems, Springer-Verlag, - ## Berlin, 1987. (pp. 86-87) + ## Reference: + ## E. Hairer, S.P. Norsett and G. Wanner, Solving Ordinary + ## Differential Equations I: Nonstiff Problems, Springer-Verlag, + ## Berlin, 1987. (pp. 86-87) if (nargin < 1 || nargin > 2) error ("gallery: 1 to 2 arguments are required for hanowa matrix."); @@ -1282,28 +1282,28 @@ function [v, beta] = house (x) ## HOUSE Householder matrix. - ## If [v, beta] = HOUSE(x) then H = EYE - beta*v*v' is a Householder - ## matrix such that Hx = -sign(x(1))*norm(x)*e_1. - ## NB: If x = 0 then v = 0, beta = 1 is returned. - ## x can be real or complex. - ## sign(x) := exp(i*arg(x)) ( = x./abs(x) when x ~= 0). + ## If [v, beta] = HOUSE(x) then H = EYE - beta*v*v' is a Householder + ## matrix such that Hx = -sign(x(1))*norm(x)*e_1. + ## NB: If x = 0 then v = 0, beta = 1 is returned. + ## x can be real or complex. + ## sign(x) := exp(i*arg(x)) ( = x./abs(x) when x ~= 0). ## - ## Theory: (textbook references Golub & Van Loan 1989, 38-43; - ## Stewart 1973, 231-234, 262; Wilkinson 1965, 48-50). - ## Hx = y: (I - beta*v*v')x = -s*e_1. - ## Must have |s| = norm(x), v = x+s*e_1, and - ## x'y = x'Hx =(x'Hx)' real => arg(s) = arg(x(1)). - ## So take s = sign(x(1))*norm(x) (which avoids cancellation). - ## v'v = (x(1)+s)^2 + x(2)^2 + ... + x(n)^2 - ## = 2*norm(x)*(norm(x) + |x(1)|). + ## Theory: (textbook references Golub & Van Loan 1989, 38-43; + ## Stewart 1973, 231-234, 262; Wilkinson 1965, 48-50). + ## Hx = y: (I - beta*v*v')x = -s*e_1. + ## Must have |s| = norm(x), v = x+s*e_1, and + ## x'y = x'Hx =(x'Hx)' real => arg(s) = arg(x(1)). + ## So take s = sign(x(1))*norm(x) (which avoids cancellation). + ## v'v = (x(1)+s)^2 + x(2)^2 + ... + x(n)^2 + ## = 2*norm(x)*(norm(x) + |x(1)|). ## - ## References: - ## G.H. Golub and C.F. Van Loan, Matrix Computations, second edition, - ## Johns Hopkins University Press, Baltimore, Maryland, 1989. - ## G.W. Stewart, Introduction to Matrix Computations, Academic Press, - ## New York, 1973, - ## J.H. Wilkinson, The Algebraic Eigenvalue Problem, Oxford University - ## Press, 1965. + ## References: + ## G.H. Golub and C.F. Van Loan, Matrix Computations, second edition, + ## Johns Hopkins University Press, Baltimore, Maryland, 1989. + ## G.W. Stewart, Introduction to Matrix Computations, Academic Press, + ## New York, 1973, + ## J.H. Wilkinson, The Algebraic Eigenvalue Problem, Oxford University + ## Press, 1965. if (nargin != 1) error ("gallery: 1 argument is required for house matrix."); @@ -1371,26 +1371,26 @@ function A = invhess (x, y) ## INVHESS Inverse of an upper Hessenberg matrix. - ## INVHESS(X, Y), where X is an N-vector and Y an N-1 vector, - ## is the matrix whose lower triangle agrees with that of - ## ONES(N,1)*X' and whose strict upper triangle agrees with - ## that of [1 Y]*ONES(1,N). - ## The matrix is nonsingular if X(1) ~= 0 and X(i+1) ~= Y(i) - ## for all i, and its inverse is an upper Hessenberg matrix. - ## If Y is omitted it defaults to -X(1:N-1). - ## Special case: if X is a scalar INVHESS(X) is the same as - ## INVHESS(1:X). + ## INVHESS(X, Y), where X is an N-vector and Y an N-1 vector, + ## is the matrix whose lower triangle agrees with that of + ## ONES(N,1)*X' and whose strict upper triangle agrees with + ## that of [1 Y]*ONES(1,N). + ## The matrix is nonsingular if X(1) ~= 0 and X(i+1) ~= Y(i) + ## for all i, and its inverse is an upper Hessenberg matrix. + ## If Y is omitted it defaults to -X(1:N-1). + ## Special case: if X is a scalar INVHESS(X) is the same as + ## INVHESS(1:X). ## - ## References: - ## F.N. Valvi and V.S. Geroyannis, Analytic inverses and - ## determinants for a class of matrices, IMA Journal of Numerical - ## Analysis, 7 (1987), pp. 123-128. - ## W.-L. Cao and W.J. Stewart, A note on inverses of Hessenberg-like - ## matrices, Linear Algebra and Appl., 76 (1986), pp. 233-240. - ## Y. Ikebe, On inverses of Hessenberg matrices, Linear Algebra and - ## Appl., 24 (1979), pp. 93-97. - ## P. Rozsa, On the inverse of band matrices, Integral Equations and - ## Operator Theory, 10 (1987), pp. 82-95. + ## References: + ## F.N. Valvi and V.S. Geroyannis, Analytic inverses and + ## determinants for a class of matrices, IMA Journal of Numerical + ## Analysis, 7 (1987), pp. 123-128. + ## W.-L. Cao and W.J. Stewart, A note on inverses of Hessenberg-like + ## matrices, Linear Algebra and Appl., 76 (1986), pp. 233-240. + ## Y. Ikebe, On inverses of Hessenberg matrices, Linear Algebra and + ## Appl., 24 (1979), pp. 93-97. + ## P. Rozsa, On the inverse of band matrices, Integral Equations and + ## Operator Theory, 10 (1987), pp. 82-95. if (nargin < 1 || nargin > 2) error ("gallery: 1 to 2 arguments are required for invhess matrix."); @@ -1425,15 +1425,15 @@ function A = invol (n) ## INVOL An involutory matrix. - ## A = INVOL(N) is an N-by-N involutory (A*A = EYE(N)) and - ## ill-conditioned matrix. - ## It is a diagonally scaled version of HILB(N). - ## NB: B = (EYE(N)-A)/2 and B = (EYE(N)+A)/2 are idempotent (B*B = B). + ## A = INVOL(N) is an N-by-N involutory (A*A = EYE(N)) and + ## ill-conditioned matrix. + ## It is a diagonally scaled version of HILB(N). + ## NB: B = (EYE(N)-A)/2 and B = (EYE(N)+A)/2 are idempotent (B*B = B). ## - ## Reference: - ## A.S. Householder and J.A. Carpenter, The singular values - ## of involutory and of idempotent matrices, Numer. Math. 5 (1963), - ## pp. 234-237. + ## Reference: + ## A.S. Householder and J.A. Carpenter, The singular values + ## of involutory and of idempotent matrices, Numer. Math. 5 (1963), + ## pp. 234-237. if (nargin != 1) error ("gallery: 1 argument is required for invol matrix."); @@ -1454,19 +1454,19 @@ function [A, detA] = ipjfact (n, k = 0) ## IPJFACT A Hankel matrix with factorial elements. - ## A = IPJFACT(N, K) is the matrix with - ## A(i,j) = (i+j)! (K = 0, default) - ## A(i,j) = 1/(i+j)! (K = 1) - ## Both are Hankel matrices. - ## The determinant and inverse are known explicitly. - ## If a second output argument is present, d = DET(A) is returned: - ## [A, d] = IPJFACT(N, K); + ## A = IPJFACT(N, K) is the matrix with + ## A(i,j) = (i+j)! (K = 0, default) + ## A(i,j) = 1/(i+j)! (K = 1) + ## Both are Hankel matrices. + ## The determinant and inverse are known explicitly. + ## If a second output argument is present, d = DET(A) is returned: + ## [A, d] = IPJFACT(N, K); ## - ## Suggested by P. R. Graves-Morris. + ## Suggested by P. R. Graves-Morris. ## - ## Reference: - ## M.J.C. Gover, The explicit inverse of factorial Hankel matrices, - ## Dept. of Mathematics, University of Bradford, 1993. + ## Reference: + ## M.J.C. Gover, The explicit inverse of factorial Hankel matrices, + ## Dept. of Mathematics, University of Bradford, 1993. if (nargin < 1 || nargin > 2) error ("gallery: 1 to 2 arguments are required for ipjfact matrix."); @@ -1515,8 +1515,8 @@ function J = jordbloc (n, lambda = 1) ## JORDBLOC Jordan block. - ## JORDBLOC(N, LAMBDA) is the N-by-N Jordan block with eigenvalue - ## LAMBDA. LAMBDA = 1 is the default. + ## JORDBLOC(N, LAMBDA) is the N-by-N Jordan block with eigenvalue + ## LAMBDA. LAMBDA = 1 is the default. if (nargin < 1 || nargin > 2) error ("gallery: 1 to 2 arguments are required for jordbloc matrix."); @@ -1531,30 +1531,30 @@ function U = kahan (n, theta = 1.2, pert = 25) ## KAHAN Kahan matrix - upper trapezoidal. - ## KAHAN(N, THETA) is an upper trapezoidal matrix - ## that has some interesting properties regarding estimation of - ## condition and rank. - ## The matrix is N-by-N unless N is a 2-vector, in which case it - ## is N(1)-by-N(2). - ## The parameter THETA defaults to 1.2. - ## The useful range of THETA is 0 < THETA < PI. + ## KAHAN(N, THETA) is an upper trapezoidal matrix + ## that has some interesting properties regarding estimation of + ## condition and rank. + ## The matrix is N-by-N unless N is a 2-vector, in which case it + ## is N(1)-by-N(2). + ## The parameter THETA defaults to 1.2. + ## The useful range of THETA is 0 < THETA < PI. ## - ## To ensure that the QR factorization with column pivoting does not - ## interchange columns in the presence of rounding errors, the diagonal - ## is perturbed by PERT*EPS*diag( [N:-1:1] ). - ## The default is PERT = 25, which ensures no interchanges for KAHAN(N) - ## up to at least N = 90 in IEEE arithmetic. - ## KAHAN(N, THETA, PERT) uses the given value of PERT. + ## To ensure that the QR factorization with column pivoting does not + ## interchange columns in the presence of rounding errors, the diagonal + ## is perturbed by PERT*EPS*diag( [N:-1:1] ). + ## The default is PERT = 25, which ensures no interchanges for KAHAN(N) + ## up to at least N = 90 in IEEE arithmetic. + ## KAHAN(N, THETA, PERT) uses the given value of PERT. ## - ## The inverse of KAHAN(N, THETA) is known explicitly: see - ## Higham (1987, p. 588), for example. - ## The diagonal perturbation was suggested by Christian Bischof. + ## The inverse of KAHAN(N, THETA) is known explicitly: see + ## Higham (1987, p. 588), for example. + ## The diagonal perturbation was suggested by Christian Bischof. ## - ## References: - ## W. Kahan, Numerical linear algebra, Canadian Math. Bulletin, - ## 9 (1966), pp. 757-801. - ## N.J. Higham, A survey of condition number estimation for - ## triangular matrices, SIAM Review, 29 (1987), pp. 575-596. + ## References: + ## W. Kahan, Numerical linear algebra, Canadian Math. Bulletin, + ## 9 (1966), pp. 757-801. + ## N.J. Higham, A survey of condition number estimation for + ## triangular matrices, SIAM Review, 29 (1987), pp. 575-596. if (nargin < 1 || nargin > 3) error ("gallery: 1 to 3 arguments are required for kahan matrix."); @@ -1584,22 +1584,22 @@ function A = kms (n, rho = 0.5) ## KMS Kac-Murdock-Szego Toeplitz matrix. - ## A = KMS(N, RHO) is the N-by-N Kac-Murdock-Szego Toeplitz matrix with - ## A(i,j) = RHO^(ABS((i-j))) (for real RHO). - ## If RHO is complex, then the same formula holds except that elements - ## below the diagonal are conjugated. - ## RHO defaults to 0.5. - ## Properties: - ## A has an LDL' factorization with - ## L = INV(TRIW(N,-RHO,1)'), - ## D(i,i) = (1-ABS(RHO)^2)*EYE(N) except D(1,1) = 1. - ## A is positive definite if and only if 0 < ABS(RHO) < 1. - ## INV(A) is tridiagonal. + ## A = KMS(N, RHO) is the N-by-N Kac-Murdock-Szego Toeplitz matrix with + ## A(i,j) = RHO^(ABS((i-j))) (for real RHO). + ## If RHO is complex, then the same formula holds except that elements + ## below the diagonal are conjugated. + ## RHO defaults to 0.5. + ## Properties: + ## A has an LDL' factorization with + ## L = INV(TRIW(N,-RHO,1)'), + ## D(i,i) = (1-ABS(RHO)^2)*EYE(N) except D(1,1) = 1. + ## A is positive definite if and only if 0 < ABS(RHO) < 1. + ## INV(A) is tridiagonal. ## - ## Reference: - ## W.F. Trench, Numerical solution of the eigenvalue problem - ## for Hermitian Toeplitz matrices, SIAM J. Matrix Analysis and Appl., - ## 10 (1989), pp. 135-146 (and see the references therein). + ## Reference: + ## W.F. Trench, Numerical solution of the eigenvalue problem + ## for Hermitian Toeplitz matrices, SIAM J. Matrix Analysis and Appl., + ## 10 (1989), pp. 135-146 (and see the references therein). if (nargin < 1 || nargin > 2) error ("gallery: 1 to 2 arguments are required for lauchli matrix."); @@ -1619,15 +1619,15 @@ function B = krylov (A, x, j) ## KRYLOV Krylov matrix. - ## KRYLOV(A, x, j) is the Krylov matrix - ## [x, Ax, A^2x, ..., A^(j-1)x], - ## where A is an n-by-n matrix and x is an n-vector. - ## Defaults: x = ONES(n,1), j = n. - ## KRYLOV(n) is the same as KRYLOV(RANDN(n)). + ## KRYLOV(A, x, j) is the Krylov matrix + ## [x, Ax, A^2x, ..., A^(j-1)x], + ## where A is an n-by-n matrix and x is an n-vector. + ## Defaults: x = ONES(n,1), j = n. + ## KRYLOV(n) is the same as KRYLOV(RANDN(n)). ## - ## Reference: - ## G.H. Golub and C.F. Van Loan, Matrix Computations, second edition, - ## Johns Hopkins University Press, Baltimore, Maryland, 1989, p. 369. + ## Reference: + ## G.H. Golub and C.F. Van Loan, Matrix Computations, second edition, + ## Johns Hopkins University Press, Baltimore, Maryland, 1989, p. 369. if (nargin < 1 || nargin > 3) error ("gallery: 1 to 3 arguments are required for krylov matrix."); @@ -1662,14 +1662,14 @@ function A = lauchli (n, mu = sqrt (eps)) ## LAUCHLI Lauchli matrix - rectangular. - ## LAUCHLI(N, MU) is the (N+1)-by-N matrix [ONES(1,N); MU*EYE(N))]. - ## It is a well-known example in least squares and other problems - ## that indicates the dangers of forming A'*A. - ## MU defaults to SQRT(EPS). + ## LAUCHLI(N, MU) is the (N+1)-by-N matrix [ONES(1,N); MU*EYE(N))]. + ## It is a well-known example in least squares and other problems + ## that indicates the dangers of forming A'*A. + ## MU defaults to SQRT(EPS). ## - ## Reference: - ## P. Lauchli, Jordan-Elimination und Ausgleichung nach - ## kleinsten Quadraten, Numer. Math, 3 (1961), pp. 226-240. + ## Reference: + ## P. Lauchli, Jordan-Elimination und Ausgleichung nach + ## kleinsten Quadraten, Numer. Math, 3 (1961), pp. 226-240. if (nargin < 1 || nargin > 2) error ("gallery: 1 to 2 arguments are required for lauchli matrix."); @@ -1685,19 +1685,19 @@ function A = lehmer (n) ## LEHMER Lehmer matrix - symmetric positive definite. - ## A = LEHMER(N) is the symmetric positive definite N-by-N matrix with - ## A(i,j) = i/j for j >= i. - ## A is totally nonnegative. INV(A) is tridiagonal, and explicit - ## formulas are known for its entries. - ## N <= COND(A) <= 4*N*N. + ## A = LEHMER(N) is the symmetric positive definite N-by-N matrix with + ## A(i,j) = i/j for j >= i. + ## A is totally nonnegative. INV(A) is tridiagonal, and explicit + ## formulas are known for its entries. + ## N <= COND(A) <= 4*N*N. ## - ## References: - ## M. Newman and J. Todd, The evaluation of matrix inversion - ## programs, J. Soc. Indust. Appl. Math., 6 (1958), pp. 466-476. - ## Solutions to problem E710 (proposed by D.H. Lehmer): The inverse - ## of a matrix, Amer. Math. Monthly, 53 (1946), pp. 534-535. - ## J. Todd, Basic Numerical Mathematics, Vol. 2: Numerical Algebra, - ## Birkhauser, Basel, and Academic Press, New York, 1977, p. 154. + ## References: + ## M. Newman and J. Todd, The evaluation of matrix inversion + ## programs, J. Soc. Indust. Appl. Math., 6 (1958), pp. 466-476. + ## Solutions to problem E710 (proposed by D.H. Lehmer): The inverse + ## of a matrix, Amer. Math. Monthly, 53 (1946), pp. 534-535. + ## J. Todd, Basic Numerical Mathematics, Vol. 2: Numerical Algebra, + ## Birkhauser, Basel, and Academic Press, New York, 1977, p. 154. if (nargin != 1) error ("gallery: 1 argument is required for lehmer matrix."); @@ -1712,23 +1712,23 @@ function T = lesp (n) ## LESP A tridiagonal matrix with real, sensitive eigenvalues. - ## LESP(N) is an N-by-N matrix whose eigenvalues are real and smoothly - ## distributed in the interval approximately [-2*N-3.5, -4.5]. - ## The sensitivities of the eigenvalues increase exponentially as - ## the eigenvalues grow more negative. - ## The matrix is similar to the symmetric tridiagonal matrix with - ## the same diagonal entries and with off-diagonal entries 1, - ## via a similarity transformation with D = diag(1!,2!,...,N!). + ## LESP(N) is an N-by-N matrix whose eigenvalues are real and smoothly + ## distributed in the interval approximately [-2*N-3.5, -4.5]. + ## The sensitivities of the eigenvalues increase exponentially as + ## the eigenvalues grow more negative. + ## The matrix is similar to the symmetric tridiagonal matrix with + ## the same diagonal entries and with off-diagonal entries 1, + ## via a similarity transformation with D = diag(1!,2!,...,N!). ## - ## References: - ## H.W.J. Lenferink and M.N. Spijker, On the use of stability regions in - ## the numerical analysis of initial value problems, - ## Math. Comp., 57 (1991), pp. 221-237. - ## L.N. Trefethen, Pseudospectra of matrices, in Numerical Analysis 1991, - ## Proceedings of the 14th Dundee Conference, - ## D.F. Griffiths and G.A. Watson, eds, Pitman Research Notes in - ## Mathematics, volume 260, Longman Scientific and Technical, Essex, - ## UK, 1992, pp. 234-266. + ## References: + ## H.W.J. Lenferink and M.N. Spijker, On the use of stability regions in + ## the numerical analysis of initial value problems, + ## Math. Comp., 57 (1991), pp. 221-237. + ## L.N. Trefethen, Pseudospectra of matrices, in Numerical Analysis 1991, + ## Proceedings of the 14th Dundee Conference, + ## D.F. Griffiths and G.A. Watson, eds, Pitman Research Notes in + ## Mathematics, volume 260, Longman Scientific and Technical, Essex, + ## UK, 1992, pp. 234-266. if (nargin != 1) error ("gallery: 1 argument is required for lesp matrix."); @@ -1742,13 +1742,13 @@ function A = lotkin (n) ## LOTKIN Lotkin matrix. - ## A = LOTKIN(N) is the Hilbert matrix with its first row altered to - ## all ones. A is unsymmetric, ill-conditioned, and has many negative - ## eigenvalues of small magnitude. - ## The inverse has integer entries and is known explicitly. + ## A = LOTKIN(N) is the Hilbert matrix with its first row altered to + ## all ones. A is unsymmetric, ill-conditioned, and has many negative + ## eigenvalues of small magnitude. + ## The inverse has integer entries and is known explicitly. ## - ## Reference: - ## M. Lotkin, A set of test matrices, MTAC, 9 (1955), pp. 153-161. + ## Reference: + ## M. Lotkin, A set of test matrices, MTAC, 9 (1955), pp. 153-161. if (nargin != 1) error ("gallery: 1 argument is required for lotkin matrix."); @@ -1762,21 +1762,21 @@ function A = minij (n) ## MINIJ Symmetric positive definite matrix MIN(i,j). - ## A = MINIJ(N) is the N-by-N symmetric positive definite matrix with - ## A(i,j) = MIN(i,j). - ## Properties, variations: - ## INV(A) is tridiagonal: it is minus the second difference matrix - ## except its (N,N) element is 1. - ## 2*A-ONES(N) (Givens' matrix) has tridiagonal inverse and - ## eigenvalues .5*sec^2([2r-1)PI/4N], r=1:N. - ## (N+1)*ONES(N)-A also has a tridiagonal inverse. + ## A = MINIJ(N) is the N-by-N symmetric positive definite matrix with + ## A(i,j) = MIN(i,j). + ## Properties, variations: + ## INV(A) is tridiagonal: it is minus the second difference matrix + ## except its (N,N) element is 1. + ## 2*A-ONES(N) (Givens' matrix) has tridiagonal inverse and + ## eigenvalues .5*sec^2([2r-1)PI/4N], r=1:N. + ## (N+1)*ONES(N)-A also has a tridiagonal inverse. ## - ## References: - ## J. Todd, Basic Numerical Mathematics, Vol. 2: Numerical Algebra, - ## Birkhauser, Basel, and Academic Press, New York, 1977, p. 158. - ## D.E. Rutherford, Some continuant determinants arising in physics and - ## chemistry---II, Proc. Royal Soc. Edin., 63, A (1952), pp. 232-241. - ## (For the eigenvalues of Givens' matrix.) + ## References: + ## J. Todd, Basic Numerical Mathematics, Vol. 2: Numerical Algebra, + ## Birkhauser, Basel, and Academic Press, New York, 1977, p. 158. + ## D.E. Rutherford, Some continuant determinants arising in physics and + ## chemistry---II, Proc. Royal Soc. Edin., 63, A (1952), pp. 232-241. + ## (For the eigenvalues of Givens' matrix.) if (nargin != 1) error ("gallery: 1 argument is required for minij matrix."); @@ -1789,17 +1789,17 @@ function A = moler (n, alpha = -1) ## MOLER Moler matrix - symmetric positive definite. - ## A = MOLER(N, ALPHA) is the symmetric positive definite N-by-N matrix - ## U'*U where U = TRIW(N, ALPHA). - ## For ALPHA = -1 (the default) A(i,j) = MIN(i,j)-2, A(i,i) = i. - ## A has one small eigenvalue. + ## A = MOLER(N, ALPHA) is the symmetric positive definite N-by-N matrix + ## U'*U where U = TRIW(N, ALPHA). + ## For ALPHA = -1 (the default) A(i,j) = MIN(i,j)-2, A(i,i) = i. + ## A has one small eigenvalue. ## - ## Nash (1990) attributes the ALPHA = -1 matrix to Moler. + ## Nash (1990) attributes the ALPHA = -1 matrix to Moler. ## - ## Reference: - ## J.C. Nash, Compact Numerical Methods for Computers: Linear - ## Algebra and Function Minimisation, second edition, Adam Hilger, - ## Bristol, 1990 (Appendix 1). + ## Reference: + ## J.C. Nash, Compact Numerical Methods for Computers: Linear + ## Algebra and Function Minimisation, second edition, Adam Hilger, + ## Bristol, 1990 (Appendix 1). if (nargin < 1 || nargin > 2) error ("gallery: 1 to 2 arguments are required for moler matrix."); @@ -1814,16 +1814,16 @@ function [A, T] = neumann (n) ## NEUMANN Singular matrix from the discrete Neumann problem (sparse). - ## NEUMANN(N) is the singular, row diagonally dominant matrix resulting - ## from discretizing the Neumann problem with the usual five point - ## operator on a regular mesh. - ## It has a one-dimensional null space with null vector ONES(N,1). - ## The dimension N should be a perfect square, or else a 2-vector, - ## in which case the dimension of the matrix is N(1)*N(2). + ## NEUMANN(N) is the singular, row diagonally dominant matrix resulting + ## from discretizing the Neumann problem with the usual five point + ## operator on a regular mesh. + ## It has a one-dimensional null space with null vector ONES(N,1). + ## The dimension N should be a perfect square, or else a 2-vector, + ## in which case the dimension of the matrix is N(1)*N(2). ## - ## Reference: - ## R.J. Plemmons, Regular splittings and the discrete Neumann - ## problem, Numer. Math., 25 (1976), pp. 153-161. + ## Reference: + ## R.J. Plemmons, Regular splittings and the discrete Neumann + ## problem, Numer. Math., 25 (1976), pp. 153-161. if (nargin != 1) error ("gallery: 1 argument is required for neumann matrix."); @@ -1890,37 +1890,37 @@ function Q = orthog (n, k = 1) ## ORTHOG Orthogonal and nearly orthogonal matrices. - ## Q = ORTHOG(N, K) selects the K'th type of matrix of order N. - ## K > 0 for exactly orthogonal matrices, K < 0 for diagonal scalings of - ## orthogonal matrices. - ## Available types: (K = 1 is the default) - ## K = 1: Q(i,j) = SQRT(2/(n+1)) * SIN( i*j*PI/(n+1) ) - ## Symmetric eigenvector matrix for second difference matrix. - ## K = 2: Q(i,j) = 2/SQRT(2*n+1)) * SIN( 2*i*j*PI/(2*n+1) ) - ## Symmetric. - ## K = 3: Q(r,s) = EXP(2*PI*i*(r-1)*(s-1)/n) / SQRT(n) (i=SQRT(-1)) - ## Unitary, the Fourier matrix. Q^4 is the identity. - ## This is essentially the same matrix as FFT(EYE(N))/SQRT(N)! - ## K = 4: Helmert matrix: a permutation of a lower Hessenberg matrix, - ## whose first row is ONES(1:N)/SQRT(N). - ## K = 5: Q(i,j) = SIN( 2*PI*(i-1)*(j-1)/n ) + COS( 2*PI*(i-1)*(j-1)/n ). - ## Symmetric matrix arising in the Hartley transform. - ## K = -1: Q(i,j) = COS( (i-1)*(j-1)*PI/(n-1) ) - ## Chebyshev Vandermonde-like matrix, based on extrema of T(n-1). - ## K = -2: Q(i,j) = COS( (i-1)*(j-1/2)*PI/n) ) - ## Chebyshev Vandermonde-like matrix, based on zeros of T(n). + ## Q = ORTHOG(N, K) selects the K'th type of matrix of order N. + ## K > 0 for exactly orthogonal matrices, K < 0 for diagonal scalings of + ## orthogonal matrices. + ## Available types: (K = 1 is the default) + ## K = 1: Q(i,j) = SQRT(2/(n+1)) * SIN( i*j*PI/(n+1) ) + ## Symmetric eigenvector matrix for second difference matrix. + ## K = 2: Q(i,j) = 2/SQRT(2*n+1)) * SIN( 2*i*j*PI/(2*n+1) ) + ## Symmetric. + ## K = 3: Q(r,s) = EXP(2*PI*i*(r-1)*(s-1)/n) / SQRT(n) (i=SQRT(-1)) + ## Unitary, the Fourier matrix. Q^4 is the identity. + ## This is essentially the same matrix as FFT(EYE(N))/SQRT(N)! + ## K = 4: Helmert matrix: a permutation of a lower Hessenberg matrix, + ## whose first row is ONES(1:N)/SQRT(N). + ## K = 5: Q(i,j) = SIN( 2*PI*(i-1)*(j-1)/n ) + COS( 2*PI*(i-1)*(j-1)/n ). + ## Symmetric matrix arising in the Hartley transform. + ## K = -1: Q(i,j) = COS( (i-1)*(j-1)*PI/(n-1) ) + ## Chebyshev Vandermonde-like matrix, based on extrema of T(n-1). + ## K = -2: Q(i,j) = COS( (i-1)*(j-1/2)*PI/n) ) + ## Chebyshev Vandermonde-like matrix, based on zeros of T(n). ## - ## References: - ## N.J. Higham and D.J. Higham, Large growth factors in Gaussian - ## elimination with pivoting, SIAM J. Matrix Analysis and Appl., - ## 10 (1989), pp. 155-164. - ## P. Morton, On the eigenvectors of Schur's matrix, J. Number Theory, - ## 12 (1980), pp. 122-127. (Re. ORTHOG(N, 3)) - ## H.O. Lancaster, The Helmert Matrices, Amer. Math. Monthly, 72 (1965), - ## pp. 4-12. - ## D. Bini and P. Favati, On a matrix algebra related to the discrete - ## Hartley transform, SIAM J. Matrix Anal. Appl., 14 (1993), - ## pp. 500-507. + ## References: + ## N.J. Higham and D.J. Higham, Large growth factors in Gaussian + ## elimination with pivoting, SIAM J. Matrix Analysis and Appl., + ## 10 (1989), pp. 155-164. + ## P. Morton, On the eigenvectors of Schur's matrix, J. Number Theory, + ## 12 (1980), pp. 122-127. (Re. ORTHOG(N, 3)) + ## H.O. Lancaster, The Helmert Matrices, Amer. Math. Monthly, 72 (1965), + ## pp. 4-12. + ## D. Bini and P. Favati, On a matrix algebra related to the discrete + ## Hartley transform, SIAM J. Matrix Anal. Appl., 14 (1993), + ## pp. 500-507. if (nargin < 1 || nargin > 2) error ("gallery: 1 to 2 arguments are required for orthog matrix."); @@ -1976,20 +1976,20 @@ function A = parter (n) ## PARTER Parter matrix - a Toeplitz matrix with singular values near PI. - ## PARTER(N) is the matrix with (i,j) element 1/(i-j+0.5). - ## It is a Cauchy matrix and a Toeplitz matrix. + ## PARTER(N) is the matrix with (i,j) element 1/(i-j+0.5). + ## It is a Cauchy matrix and a Toeplitz matrix. ## - ## At the Second SIAM Conference on Linear Algebra, Raleigh, N.C., - ## 1985, Cleve Moler noted that most of the singular values of - ## PARTER(N) are very close to PI. An explanation of the phenomenon - ## was given by Parter; see also the paper by Tyrtyshnikov. + ## At the Second SIAM Conference on Linear Algebra, Raleigh, N.C., + ## 1985, Cleve Moler noted that most of the singular values of + ## PARTER(N) are very close to PI. An explanation of the phenomenon + ## was given by Parter; see also the paper by Tyrtyshnikov. ## - ## References: - ## The MathWorks Newsletter, Volume 1, Issue 1, March 1986, page 2. - ## S.V. Parter, On the distribution of the singular values of Toeplitz - ## matrices, Linear Algebra and Appl., 80 (1986), pp. 115-130. - ## E.E. Tyrtyshnikov, Cauchy-Toeplitz matrices and some applications, - ## Linear Algebra and Appl., 149 (1991), pp. 1-18. + ## References: + ## The MathWorks Newsletter, Volume 1, Issue 1, March 1986, page 2. + ## S.V. Parter, On the distribution of the singular values of Toeplitz + ## matrices, Linear Algebra and Appl., 80 (1986), pp. 115-130. + ## E.E. Tyrtyshnikov, Cauchy-Toeplitz matrices and some applications, + ## Linear Algebra and Appl., 149 (1991), pp. 1-18. if (nargin != 1) error ("gallery: 1 argument is required for parter matrix."); @@ -2002,14 +2002,14 @@ function P = pei (n, alpha = 1) ## PEI Pei matrix. - ## PEI(N, ALPHA), where ALPHA is a scalar, is the symmetric matrix - ## ALPHA*EYE(N) + ONES(N). - ## If ALPHA is omitted then ALPHA = 1 is used. - ## The matrix is singular for ALPHA = 0, -N. + ## PEI(N, ALPHA), where ALPHA is a scalar, is the symmetric matrix + ## ALPHA*EYE(N) + ONES(N). + ## If ALPHA is omitted then ALPHA = 1 is used. + ## The matrix is singular for ALPHA = 0, -N. ## - ## Reference: - ## M.L. Pei, A test matrix for inversion procedures, - ## Comm. ACM, 5 (1962), p. 508. + ## Reference: + ## M.L. Pei, A test matrix for inversion procedures, + ## Comm. ACM, 5 (1962), p. 508. if (nargin < 1 || nargin > 2) error ("gallery: 1 to 2 arguments are required for pei matrix."); @@ -2024,14 +2024,14 @@ function A = poisson (n) ## POISSON Block tridiagonal matrix from Poisson's equation (sparse). - ## POISSON(N) is the block tridiagonal matrix of order N^2 - ## resulting from discretizing Poisson's equation with the - ## 5-point operator on an N-by-N mesh. + ## POISSON(N) is the block tridiagonal matrix of order N^2 + ## resulting from discretizing Poisson's equation with the + ## 5-point operator on an N-by-N mesh. ## - ## Reference: - ## G.H. Golub and C.F. Van Loan, Matrix Computations, second edition, - ## Johns Hopkins University Press, Baltimore, Maryland, 1989 - ## (Section 4.5.4). + ## Reference: + ## G.H. Golub and C.F. Van Loan, Matrix Computations, second edition, + ## Johns Hopkins University Press, Baltimore, Maryland, 1989 + ## (Section 4.5.4). if (nargin != 1) error ("gallery: 1 argument is required for poisson matrix."); @@ -2046,17 +2046,17 @@ function A = prolate (n, w = 0.25) ## PROLATE Prolate matrix - symmetric, ill-conditioned Toeplitz matrix. - ## A = PROLATE(N, W) is the N-by-N prolate matrix with parameter W. - ## It is a symmetric Toeplitz matrix. - ## If 0 < W < 0.5 then - ## - A is positive definite - ## - the eigenvalues of A are distinct, lie in (0, 1), and - ## tend to cluster around 0 and 1. - ## W defaults to 0.25. + ## A = PROLATE(N, W) is the N-by-N prolate matrix with parameter W. + ## It is a symmetric Toeplitz matrix. + ## If 0 < W < 0.5 then + ## - A is positive definite + ## - the eigenvalues of A are distinct, lie in (0, 1), and + ## tend to cluster around 0 and 1. + ## W defaults to 0.25. ## - ## Reference: - ## J.M. Varah. The Prolate matrix. Linear Algebra and Appl., - ## 187:269--278, 1993. + ## Reference: + ## J.M. Varah. The Prolate matrix. Linear Algebra and Appl., + ## 187:269--278, 1993. if (nargin < 1 || nargin > 2) error ("gallery: 1 to 2 arguments are required for prolate matrix."); @@ -2076,23 +2076,23 @@ function H = randhess (x) ## NOTE: this function was named ohess in the original Test Matrix Toolbox ## RANDHESS Random, orthogonal upper Hessenberg matrix. - ## H = RANDHESS(N) is an N-by-N real, random, orthogonal - ## upper Hessenberg matrix. - ## Alternatively, H = RANDHESS(X), where X is an arbitrary real - ## N-vector (N > 1) constructs H non-randomly using the elements - ## of X as parameters. - ## In both cases H is constructed via a product of N-1 Givens rotations. + ## H = RANDHESS(N) is an N-by-N real, random, orthogonal + ## upper Hessenberg matrix. + ## Alternatively, H = RANDHESS(X), where X is an arbitrary real + ## N-vector (N > 1) constructs H non-randomly using the elements + ## of X as parameters. + ## In both cases H is constructed via a product of N-1 Givens rotations. ## - ## Note: See Gragg (1986) for how to represent an N-by-N (complex) - ## unitary Hessenberg matrix with positive subdiagonal elements in terms - ## of 2N-1 real parameters (the Schur parametrization). - ## This M-file handles the real case only and is intended simply as a - ## convenient way to generate random or non-random orthogonal Hessenberg - ## matrices. + ## Note: See Gragg (1986) for how to represent an N-by-N (complex) + ## unitary Hessenberg matrix with positive subdiagonal elements in terms + ## of 2N-1 real parameters (the Schur parametrization). + ## This M-file handles the real case only and is intended simply as a + ## convenient way to generate random or non-random orthogonal Hessenberg + ## matrices. ## - ## Reference: - ## W.B. Gragg, The QR algorithm for unitary Hessenberg matrices, - ## J. Comp. Appl. Math., 16 (1986), pp. 1-8. + ## Reference: + ## W.B. Gragg, The QR algorithm for unitary Hessenberg matrices, + ## J. Comp. Appl. Math., 16 (1986), pp. 1-8. if (nargin != 1) error ("gallery: 1 argument is required for randhess matrix."); @@ -2125,12 +2125,12 @@ function A = rando (n, k = 1) ## RANDO Random matrix with elements -1, 0 or 1. - ## A = RANDO(N, K) is a random N-by-N matrix with elements from - ## one of the following discrete distributions (default K = 1): - ## K = 1: A(i,j) = 0 or 1 with equal probability, - ## K = 2: A(i,j) = -1 or 1 with equal probability, - ## K = 3: A(i,j) = -1, 0 or 1 with equal probability. - ## N may be a 2-vector, in which case the matrix is N(1)-by-N(2). + ## A = RANDO(N, K) is a random N-by-N matrix with elements from + ## one of the following discrete distributions (default K = 1): + ## K = 1: A(i,j) = 0 or 1 with equal probability, + ## K = 2: A(i,j) = -1 or 1 with equal probability, + ## K = 3: A(i,j) = -1, 0 or 1 with equal probability. + ## N may be a 2-vector, in which case the matrix is N(1)-by-N(2). if (nargin < 1 || nargin > 2) error ("gallery: 1 to 2 arguments are required for rando matrix."); @@ -2156,37 +2156,37 @@ function A = randsvd (n, kappa = sqrt (1/eps), mode = 3, kl = n-1, ku = kl) ## RANDSVD Random matrix with pre-assigned singular values. - ## RANDSVD(N, KAPPA, MODE, KL, KU) is a (banded) random matrix of order N - ## with COND(A) = KAPPA and singular values from the distribution MODE. - ## N may be a 2-vector, in which case the matrix is N(1)-by-N(2). - ## Available types: - ## MODE = 1: one large singular value, - ## MODE = 2: one small singular value, - ## MODE = 3: geometrically distributed singular values, - ## MODE = 4: arithmetically distributed singular values, - ## MODE = 5: random singular values with unif. dist. logarithm. - ## If omitted, MODE defaults to 3, and KAPPA defaults to SQRT(1/EPS). - ## If MODE < 0 then the effect is as for ABS(MODE) except that in the - ## original matrix of singular values the order of the diagonal entries - ## is reversed: small to large instead of large to small. - ## KL and KU are the lower and upper bandwidths respectively; if they - ## are omitted a full matrix is produced. - ## If only KL is present, KU defaults to KL. - ## Special case: if KAPPA < 0 then a random full symmetric positive - ## definite matrix is produced with COND(A) = -KAPPA and - ## eigenvalues distributed according to MODE. - ## KL and KU, if present, are ignored. + ## RANDSVD(N, KAPPA, MODE, KL, KU) is a (banded) random matrix of order N + ## with COND(A) = KAPPA and singular values from the distribution MODE. + ## N may be a 2-vector, in which case the matrix is N(1)-by-N(2). + ## Available types: + ## MODE = 1: one large singular value, + ## MODE = 2: one small singular value, + ## MODE = 3: geometrically distributed singular values, + ## MODE = 4: arithmetically distributed singular values, + ## MODE = 5: random singular values with unif. dist. logarithm. + ## If omitted, MODE defaults to 3, and KAPPA defaults to SQRT(1/EPS). + ## If MODE < 0 then the effect is as for ABS(MODE) except that in the + ## original matrix of singular values the order of the diagonal entries + ## is reversed: small to large instead of large to small. + ## KL and KU are the lower and upper bandwidths respectively; if they + ## are omitted a full matrix is produced. + ## If only KL is present, KU defaults to KL. + ## Special case: if KAPPA < 0 then a random full symmetric positive + ## definite matrix is produced with COND(A) = -KAPPA and + ## eigenvalues distributed according to MODE. + ## KL and KU, if present, are ignored. ## - ## Reference: - ## N.J. Higham, Accuracy and Stability of Numerical Algorithms, - ## Society for Industrial and Applied Mathematics, Philadelphia, PA, - ## USA, 1996; sec. 26.3. + ## Reference: + ## N.J. Higham, Accuracy and Stability of Numerical Algorithms, + ## Society for Industrial and Applied Mathematics, Philadelphia, PA, + ## USA, 1996; sec. 26.3. ## - ## This routine is similar to the more comprehensive Fortran routine xLATMS - ## in the following reference: - ## J.W. Demmel and A. McKenney, A test matrix generation suite, - ## LAPACK Working Note #9, Courant Institute of Mathematical Sciences, - ## New York, 1989. + ## This routine is similar to the more comprehensive Fortran routine xLATMS + ## in the following reference: + ## J.W. Demmel and A. McKenney, A test matrix generation suite, + ## LAPACK Working Note #9, Courant Institute of Mathematical Sciences, + ## New York, 1989. if (nargin < 1 || nargin > 5) error ("gallery: 1 to 5 arguments are required for randsvd matrix."); @@ -2245,7 +2245,7 @@ ## Convert to diagonal matrix of singular values. if (mode < 0) - sigma = sigma (p:-1:1); + sigma = sigma(p:-1:1); endif sigma = diag (sigma); @@ -2280,28 +2280,28 @@ function A = redheff (n) ## REDHEFF A (0,1) matrix of Redheffer associated with the Riemann hypothesis. - ## A = REDHEFF(N) is an N-by-N matrix of 0s and 1s defined by - ## A(i,j) = 1 if j = 1 or if i divides j, - ## A(i,j) = 0 otherwise. - ## It has N - FLOOR(LOG2(N)) - 1 eigenvalues equal to 1, - ## a real eigenvalue (the spectral radius) approximately SQRT(N), - ## a negative eigenvalue approximately -SQRT(N), - ## and the remaining eigenvalues are provably ``small''. - ## Barrett and Jarvis (1992) conjecture that - ## ``the small eigenvalues all lie inside the unit circle - ## ABS(Z) = 1'', - ## and a proof of this conjecture, together with a proof that some - ## eigenvalue tends to zero as N tends to infinity, would yield - ## a new proof of the prime number theorem. - ## The Riemann hypothesis is true if and only if - ## DET(A) = O( N^(1/2+epsilon) ) for every epsilon > 0 - ## (`!' denotes factorial). - ## See also RIEMANN. + ## A = REDHEFF(N) is an N-by-N matrix of 0s and 1s defined by + ## A(i,j) = 1 if j = 1 or if i divides j, + ## A(i,j) = 0 otherwise. + ## It has N - FLOOR(LOG2(N)) - 1 eigenvalues equal to 1, + ## a real eigenvalue (the spectral radius) approximately SQRT(N), + ## a negative eigenvalue approximately -SQRT(N), + ## and the remaining eigenvalues are provably ``small''. + ## Barrett and Jarvis (1992) conjecture that + ## ``the small eigenvalues all lie inside the unit circle + ## ABS(Z) = 1'', + ## and a proof of this conjecture, together with a proof that some + ## eigenvalue tends to zero as N tends to infinity, would yield + ## a new proof of the prime number theorem. + ## The Riemann hypothesis is true if and only if + ## DET(A) = O( N^(1/2+epsilon) ) for every epsilon > 0 + ## (`!' denotes factorial). + ## See also RIEMANN. ## - ## Reference: - ## W.W. Barrett and T.J. Jarvis, - ## Spectral Properties of a Matrix of Redheffer, - ## Linear Algebra and Appl., 162 (1992), pp. 673-683. + ## Reference: + ## W.W. Barrett and T.J. Jarvis, + ## Spectral Properties of a Matrix of Redheffer, + ## Linear Algebra and Appl., 162 (1992), pp. 673-683. if (nargin != 1) error ("gallery: 1 argument is required for redheff matrix."); @@ -2316,22 +2316,22 @@ function A = riemann (n) ## RIEMANN A matrix associated with the Riemann hypothesis. - ## A = RIEMANN(N) is an N-by-N matrix for which the - ## Riemann hypothesis is true if and only if - ## DET(A) = O( N! N^(-1/2+epsilon) ) for every epsilon > 0 - ## (`!' denotes factorial). - ## A = B(2:N+1, 2:N+1), where - ## B(i,j) = i-1 if i divides j and -1 otherwise. - ## Properties include, with M = N+1: - ## Each eigenvalue E(i) satisfies ABS(E(i)) <= M - 1/M. - ## i <= E(i) <= i+1 with at most M-SQRT(M) exceptions. - ## All integers in the interval (M/3, M/2] are eigenvalues. + ## A = RIEMANN(N) is an N-by-N matrix for which the + ## Riemann hypothesis is true if and only if + ## DET(A) = O( N! N^(-1/2+epsilon) ) for every epsilon > 0 + ## (`!' denotes factorial). + ## A = B(2:N+1, 2:N+1), where + ## B(i,j) = i-1 if i divides j and -1 otherwise. + ## Properties include, with M = N+1: + ## Each eigenvalue E(i) satisfies ABS(E(i)) <= M - 1/M. + ## i <= E(i) <= i+1 with at most M-SQRT(M) exceptions. + ## All integers in the interval (M/3, M/2] are eigenvalues. ## - ## See also REDHEFF. + ## See also REDHEFF. ## - ## Reference: - ## F. Roesler, Riemann's hypothesis as an eigenvalue problem, - ## Linear Algebra and Appl., 81 (1986), pp. 153-198. + ## Reference: + ## F. Roesler, Riemann's hypothesis as an eigenvalue problem, + ## Linear Algebra and Appl., 81 (1986), pp. 153-198. if (nargin != 1) error ("gallery: 1 argument is required for riemann matrix."); @@ -2348,16 +2348,16 @@ function A = ris (n) ## NOTE: this function was named dingdong in the original Test Matrix Toolbox ## RIS Dingdong matrix - a symmetric Hankel matrix. - ## A = RIS(N) is the symmetric N-by-N Hankel matrix with - ## A(i,j) = 0.5/(N-i-j+1.5). - ## The eigenvalues of A cluster around PI/2 and -PI/2. + ## A = RIS(N) is the symmetric N-by-N Hankel matrix with + ## A(i,j) = 0.5/(N-i-j+1.5). + ## The eigenvalues of A cluster around PI/2 and -PI/2. ## - ## Invented by F.N. Ris. + ## Invented by F.N. Ris. ## - ## Reference: - ## J.C. Nash, Compact Numerical Methods for Computers: Linear - ## Algebra and Function Minimisation, second edition, Adam Hilger, - ## Bristol, 1990 (Appendix 1). + ## Reference: + ## J.C. Nash, Compact Numerical Methods for Computers: Linear + ## Algebra and Function Minimisation, second edition, Adam Hilger, + ## Bristol, 1990 (Appendix 1). if (nargin != 1) error ("gallery: 1 argument is required for ris matrix."); @@ -2371,20 +2371,20 @@ function A = smoke (n, k = 0) ## SMOKE Smoke matrix - complex, with a `smoke ring' pseudospectrum. - ## SMOKE(N) is an N-by-N matrix with 1s on the - ## superdiagonal, 1 in the (N,1) position, and powers of - ## roots of unity along the diagonal. - ## SMOKE(N, 1) is the same except for a zero (N,1) element. - ## The eigenvalues of SMOKE(N, 1) are the N'th roots of unity; - ## those of SMOKE(N) are the N'th roots of unity times 2^(1/N). + ## SMOKE(N) is an N-by-N matrix with 1s on the + ## superdiagonal, 1 in the (N,1) position, and powers of + ## roots of unity along the diagonal. + ## SMOKE(N, 1) is the same except for a zero (N,1) element. + ## The eigenvalues of SMOKE(N, 1) are the N'th roots of unity; + ## those of SMOKE(N) are the N'th roots of unity times 2^(1/N). ## - ## Try PS(SMOKE(32)). For SMOKE(N, 1) the pseudospectrum looks - ## like a sausage folded back on itself. - ## GERSH(SMOKE(N, 1)) is interesting. + ## Try PS(SMOKE(32)). For SMOKE(N, 1) the pseudospectrum looks + ## like a sausage folded back on itself. + ## GERSH(SMOKE(N, 1)) is interesting. ## - ## Reference: - ## L. Reichel and L.N. Trefethen, Eigenvalues and pseudo-eigenvalues of - ## Toeplitz matrices, Linear Algebra and Appl., 162-164:153-185, 1992. + ## Reference: + ## L. Reichel and L.N. Trefethen, Eigenvalues and pseudo-eigenvalues of + ## Toeplitz matrices, Linear Algebra and Appl., 162-164:153-185, 1992. if (nargin < 1 || nargin > 2) error ("gallery: 1 to 2 arguments are required for smoke matrix."); @@ -2408,18 +2408,18 @@ function T = toeppd (n, m = n, w = rand (m,1), theta = rand (m,1)) ## NOTE: this function was named pdtoep in the original Test Matrix Toolbox ## TOEPPD Symmetric positive definite Toeplitz matrix. - ## TOEPPD(N, M, W, THETA) is an N-by-N symmetric positive (semi-) - ## definite (SPD) Toeplitz matrix, comprised of the sum of M rank 2 - ## (or, for certain THETA, rank 1) SPD Toeplitz matrices. - ## Specifically, - ## T = W(1)*T(THETA(1)) + ... + W(M)*T(THETA(M)), - ## where T(THETA(k)) has (i,j) element COS(2*PI*THETA(k)*(i-j)). - ## Defaults: M = N, W = RAND(M,1), THETA = RAND(M,1). + ## TOEPPD(N, M, W, THETA) is an N-by-N symmetric positive (semi-) + ## definite (SPD) Toeplitz matrix, comprised of the sum of M rank 2 + ## (or, for certain THETA, rank 1) SPD Toeplitz matrices. + ## Specifically, + ## T = W(1)*T(THETA(1)) + ... + W(M)*T(THETA(M)), + ## where T(THETA(k)) has (i,j) element COS(2*PI*THETA(k)*(i-j)). + ## Defaults: M = N, W = RAND(M,1), THETA = RAND(M,1). ## - ## Reference: - ## G. Cybenko and C.F. Van Loan, Computing the minimum eigenvalue of - ## a symmetric positive definite Toeplitz matrix, SIAM J. Sci. Stat. - ## Comput., 7 (1986), pp. 123-131. + ## Reference: + ## G. Cybenko and C.F. Van Loan, Computing the minimum eigenvalue of + ## a symmetric positive definite Toeplitz matrix, SIAM J. Sci. Stat. + ## Comput., 7 (1986), pp. 123-131. if (nargin < 1 || nargin > 4) error ("gallery: 1 to 4 arguments are required for toeppd matrix."); @@ -2442,25 +2442,25 @@ function P = toeppen (n, a = 1, b = -10, c = 0, d = 10, e = 1) ## NOTE: this function was named pentoep in the original Test Matrix Toolbox ## TOEPPEN Pentadiagonal Toeplitz matrix (sparse). - ## P = TOEPPEN(N, A, B, C, D, E) is the N-by-N pentadiagonal - ## Toeplitz matrix with diagonals composed of the numbers - ## A =: P(3,1), B =: P(2,1), C =: P(1,1), D =: P(1,2), E =: P(1,3). - ## Default: (A,B,C,D,E) = (1,-10,0,10,1) (a matrix of Rutishauser). - ## This matrix has eigenvalues lying approximately on - ## the line segment 2*cos(2*t) + 20*i*sin(t). + ## P = TOEPPEN(N, A, B, C, D, E) is the N-by-N pentadiagonal + ## Toeplitz matrix with diagonals composed of the numbers + ## A =: P(3,1), B =: P(2,1), C =: P(1,1), D =: P(1,2), E =: P(1,3). + ## Default: (A,B,C,D,E) = (1,-10,0,10,1) (a matrix of Rutishauser). + ## This matrix has eigenvalues lying approximately on + ## the line segment 2*cos(2*t) + 20*i*sin(t). ## - ## Interesting plots are - ## PS(FULL(TOEPPEN(32,0,1,0,0,1/4))) - `triangle' - ## PS(FULL(TOEPPEN(32,0,1/2,0,0,1))) - `propeller' - ## PS(FULL(TOEPPEN(32,0,1/2,1,1,1))) - `fish' + ## Interesting plots are + ## PS(FULL(TOEPPEN(32,0,1,0,0,1/4))) - `triangle' + ## PS(FULL(TOEPPEN(32,0,1/2,0,0,1))) - `propeller' + ## PS(FULL(TOEPPEN(32,0,1/2,1,1,1))) - `fish' ## - ## References: - ## R.M. Beam and R.F. Warming, The asymptotic spectra of - ## banded Toeplitz and quasi-Toeplitz matrices, SIAM J. Sci. - ## Comput. 14 (4), 1993, pp. 971-1006. - ## H. Rutishauser, On test matrices, Programmation en Mathematiques - ## Numeriques, Editions Centre Nat. Recherche Sci., Paris, 165, - ## 1966, pp. 349-365. + ## References: + ## R.M. Beam and R.F. Warming, The asymptotic spectra of + ## banded Toeplitz and quasi-Toeplitz matrices, SIAM J. Sci. + ## Comput. 14 (4), 1993, pp. 971-1006. + ## H. Rutishauser, On test matrices, Programmation en Mathematiques + ## Numeriques, Editions Centre Nat. Recherche Sci., Paris, 165, + ## 1966, pp. 349-365. if (nargin < 1 || nargin > 6) error ("gallery: 1 to 6 arguments are required for toeppen matrix."); @@ -2476,23 +2476,23 @@ function T = tridiag (n, x = -1, y = 2, z = -1) ## TRIDIAG Tridiagonal matrix (sparse). - ## TRIDIAG(X, Y, Z) is the tridiagonal matrix with subdiagonal X, - ## diagonal Y, and superdiagonal Z. - ## X and Z must be vectors of dimension one less than Y. - ## Alternatively TRIDIAG(N, C, D, E), where C, D, and E are all - ## scalars, yields the Toeplitz tridiagonal matrix of order N - ## with subdiagonal elements C, diagonal elements D, and superdiagonal - ## elements E. This matrix has eigenvalues (Todd 1977) - ## D + 2*SQRT(C*E)*COS(k*PI/(N+1)), k=1:N. - ## TRIDIAG(N) is the same as TRIDIAG(N,-1,2,-1), which is - ## a symmetric positive definite M-matrix (the negative of the - ## second difference matrix). + ## TRIDIAG(X, Y, Z) is the tridiagonal matrix with subdiagonal X, + ## diagonal Y, and superdiagonal Z. + ## X and Z must be vectors of dimension one less than Y. + ## Alternatively TRIDIAG(N, C, D, E), where C, D, and E are all + ## scalars, yields the Toeplitz tridiagonal matrix of order N + ## with subdiagonal elements C, diagonal elements D, and superdiagonal + ## elements E. This matrix has eigenvalues (Todd 1977) + ## D + 2*SQRT(C*E)*COS(k*PI/(N+1)), k=1:N. + ## TRIDIAG(N) is the same as TRIDIAG(N,-1,2,-1), which is + ## a symmetric positive definite M-matrix (the negative of the + ## second difference matrix). ## - ## References: - ## J. Todd, Basic Numerical Mathematics, Vol. 2: Numerical Algebra, - ## Birkhauser, Basel, and Academic Press, New York, 1977, p. 155. - ## D.E. Rutherford, Some continuant determinants arising in physics and - ## chemistry---II, Proc. Royal Soc. Edin., 63, A (1952), pp. 232-241. + ## References: + ## J. Todd, Basic Numerical Mathematics, Vol. 2: Numerical Algebra, + ## Birkhauser, Basel, and Academic Press, New York, 1977, p. 155. + ## D.E. Rutherford, Some continuant determinants arising in physics and + ## chemistry---II, Proc. Royal Soc. Edin., 63, A (1952), pp. 232-241. if (nargin != 1 && nargin != 3 && nargin != 4) error ("gallery: 1, 3, or 4 arguments are required for tridiag matrix."); @@ -2524,33 +2524,33 @@ function t = triw (n, alpha = -1, k = n(end) - 1) ## TRIW Upper triangular matrix discussed by Wilkinson and others. - ## TRIW(N, ALPHA, K) is the upper triangular matrix with ones on - ## the diagonal and ALPHAs on the first K >= 0 superdiagonals. - ## N may be a 2-vector, in which case the matrix is N(1)-by-N(2) and - ## upper trapezoidal. - ## Defaults: ALPHA = -1, - ## K = N - 1 (full upper triangle). - ## TRIW(N) is a matrix discussed by Kahan, Golub and Wilkinson. + ## TRIW(N, ALPHA, K) is the upper triangular matrix with ones on + ## the diagonal and ALPHAs on the first K >= 0 superdiagonals. + ## N may be a 2-vector, in which case the matrix is N(1)-by-N(2) and + ## upper trapezoidal. + ## Defaults: ALPHA = -1, + ## K = N - 1 (full upper triangle). + ## TRIW(N) is a matrix discussed by Kahan, Golub and Wilkinson. ## - ## Ostrowski (1954) shows that - ## COND(TRIW(N,2)) = COT(PI/(4*N))^2, - ## and for large ABS(ALPHA), - ## COND(TRIW(N,ALPHA)) is approximately ABS(ALPHA)^N*SIN(PI/(4*N-2)). + ## Ostrowski (1954) shows that + ## COND(TRIW(N,2)) = COT(PI/(4*N))^2, + ## and for large ABS(ALPHA), + ## COND(TRIW(N,ALPHA)) is approximately ABS(ALPHA)^N*SIN(PI/(4*N-2)). ## - ## Adding -2^(2-N) to the (N,1) element makes TRIW(N) singular, - ## as does adding -2^(1-N) to all elements in the first column. + ## Adding -2^(2-N) to the (N,1) element makes TRIW(N) singular, + ## as does adding -2^(1-N) to all elements in the first column. ## - ## References: - ## G.H. Golub and J.H. Wilkinson, Ill-conditioned eigensystems and the - ## computation of the Jordan canonical form, SIAM Review, - ## 18(4), 1976, pp. 578-619. - ## W. Kahan, Numerical linear algebra, Canadian Math. Bulletin, - ## 9 (1966), pp. 757-801. - ## A.M. Ostrowski, On the spectrum of a one-parametric family of - ## matrices, J. Reine Angew. Math., 193 (3/4), 1954, pp. 143-160. - ## J.H. Wilkinson, Singular-value decomposition---basic aspects, - ## in D.A.H. Jacobs, ed., Numerical Software---Needs and Availability, - ## Academic Press, London, 1978, pp. 109-135. + ## References: + ## G.H. Golub and J.H. Wilkinson, Ill-conditioned eigensystems and the + ## computation of the Jordan canonical form, SIAM Review, + ## 18(4), 1976, pp. 578-619. + ## W. Kahan, Numerical linear algebra, Canadian Math. Bulletin, + ## 9 (1966), pp. 757-801. + ## A.M. Ostrowski, On the spectrum of a one-parametric family of + ## matrices, J. Reine Angew. Math., 193 (3/4), 1954, pp. 143-160. + ## J.H. Wilkinson, Singular-value decomposition---basic aspects, + ## in D.A.H. Jacobs, ed., Numerical Software---Needs and Availability, + ## Academic Press, London, 1978, pp. 109-135. if (nargin < 1 || nargin > 3) error ("gallery: 1 to 3 arguments are required for triw matrix."); @@ -2610,79 +2610,79 @@ endfunction function A = wathen (nx, ny, k = 0) - ## # WATHEN returns the Wathen matrix. + ## WATHEN returns the Wathen matrix. ## - ## Discussion: + ## Discussion: ## - ## The Wathen matrix is a finite element matrix which is sparse. + ## The Wathen matrix is a finite element matrix which is sparse. ## - ## The entries of the matrix depend in part on a physical quantity - ## related to density. That density is here assigned random values between - ## 0 and 100. + ## The entries of the matrix depend in part on a physical quantity + ## related to density. That density is here assigned random values between + ## 0 and 100. ## - ## A = WATHEN ( NX, NY ) is a sparse random N-by-N finite element matrix - ## where N = 3*NX*NY + 2*NX + 2*NY + 1. + ## A = WATHEN ( NX, NY ) is a sparse random N-by-N finite element matrix + ## where N = 3*NX*NY + 2*NX + 2*NY + 1. ## - ## A is the consistent mass matrix for a regular NX-by-NY - ## grid of 8-node (serendipity) elements in 2 space dimensions. + ## A is the consistent mass matrix for a regular NX-by-NY + ## grid of 8-node (serendipity) elements in 2 space dimensions. ## - ## Here is an illustration for NX = 3, NX = 2: + ## Here is an illustration for NX = 3, NX = 2: ## - ## 23-24-25-26-27-28-29 - ## | | | | - ## 19 20 21 22 - ## | | | | - ## 12-13-14-15-16-17-18 - ## | | | | - ## 8 9 10 11 - ## | | | | - ## 1--2--3--4--5--6--7 + ## 23-24-25-26-27-28-29 + ## | | | | + ## 19 20 21 22 + ## | | | | + ## 12-13-14-15-16-17-18 + ## | | | | + ## 8 9 10 11 + ## | | | | + ## 1--2--3--4--5--6--7 ## - ## For this example, the total number of nodes is, as expected, + ## For this example, the total number of nodes is, as expected, ## - ## N = 3 * 3 * 2 + 2 * 2 + 2 * 3 + 1 = 29. + ## N = 3 * 3 * 2 + 2 * 2 + 2 * 3 + 1 = 29. ## - ## A is symmetric positive definite for any (positive) values of - ## the density, RHO(NX,NY), which is chosen randomly in this routine. + ## A is symmetric positive definite for any (positive) values of + ## the density, RHO(NX,NY), which is chosen randomly in this routine. ## - ## In particular, if D = DIAG(DIAG(A)), then - ## 0.25 <= EIG(INV(D)*A) <= 4.5 - ## for any positive integers NX and NY and any densities RHO(NX,NY). + ## In particular, if D = DIAG(DIAG(A)), then + ## 0.25 <= EIG(INV(D)*A) <= 4.5 + ## for any positive integers NX and NY and any densities RHO(NX,NY). ## - ## A = WATHEN ( NX, NY, 1 ) returns the diagonally scaled matrix. + ## A = WATHEN ( NX, NY, 1 ) returns the diagonally scaled matrix. ## - ## Modified: + ## Modified: ## - ## 17 September 2007 + ## 17 September 2007 ## - ## Author: + ## Author: ## - ## Nicholas Higham + ## Nicholas Higham ## - ## Reference: + ## Reference: ## - ## Nicholas Higham, - ## Algorithm 694: A Collection of Test Matrices in MATLAB, - ## ACM Transactions on Mathematical Software, - ## Volume 17, Number 3, September 1991, pages 289-305. + ## Nicholas Higham, + ## Algorithm 694: A Collection of Test Matrices in MATLAB, + ## ACM Transactions on Mathematical Software, + ## Volume 17, Number 3, September 1991, pages 289-305. ## - ## Andrew Wathen, - ## Realistic eigenvalue bounds for the Galerkin mass matrix, - ## IMA Journal of Numerical Analysis, - ## Volume 7, 1987, pages 449-457. + ## Andrew Wathen, + ## Realistic eigenvalue bounds for the Galerkin mass matrix, + ## IMA Journal of Numerical Analysis, + ## Volume 7, 1987, pages 449-457. ## - ## Parameters: + ## Parameters: ## - ## Input, integer NX, NY, the number of elements in the X and Y directions - ## of the finite element grid. NX and NY must each be at least 1. + ## Input, integer NX, NY, the number of elements in the X and Y directions + ## of the finite element grid. NX and NY must each be at least 1. ## - ## Optional input, integer K, is used to request that the diagonally scaled - ## version of the matrix be returned. This happens if K is specified with - ## the value 1. + ## Optional input, integer K, is used to request that the diagonally scaled + ## version of the matrix be returned. This happens if K is specified with + ## the value 1. ## - ## Output, sparse real A(N,N), the matrix. The dimension N is determined by - ## NX and NY, as described above. A is stored in the MATLAB sparse matrix - ## format. + ## Output, sparse real A(N,N), the matrix. The dimension N is determined by + ## NX and NY, as described above. A is stored in the MATLAB sparse matrix + ## format. if (nargin < 2 || nargin > 3) error ("gallery: 2 or 3 arguments are required for wathen matrix."); @@ -2746,19 +2746,19 @@ function [A, b] = wilk (n) ## WILK Various specific matrices devised/discussed by Wilkinson. - ## [A, b] = WILK(N) is the matrix or system of order N. - ## N = 3: upper triangular system Ux=b illustrating inaccurate solution. - ## N = 4: lower triangular system Lx=b, ill-conditioned. - ## N = 5: HILB(6)(1:5,2:6)*1.8144. Symmetric positive definite. - ## N = 21: W21+, tridiagonal. Eigenvalue problem. + ## [A, b] = WILK(N) is the matrix or system of order N. + ## N = 3: upper triangular system Ux=b illustrating inaccurate solution. + ## N = 4: lower triangular system Lx=b, ill-conditioned. + ## N = 5: HILB(6)(1:5,2:6)*1.8144. Symmetric positive definite. + ## N = 21: W21+, tridiagonal. Eigenvalue problem. ## - ## References: - ## J.H. Wilkinson, Error analysis of direct methods of matrix inversion, - ## J. Assoc. Comput. Mach., 8 (1961), pp. 281-330. - ## J.H. Wilkinson, Rounding Errors in Algebraic Processes, Notes on Applied - ## Science No. 32, Her Majesty's Stationery Office, London, 1963. - ## J.H. Wilkinson, The Algebraic Eigenvalue Problem, Oxford University - ## Press, 1965. + ## References: + ## J.H. Wilkinson, Error analysis of direct methods of matrix inversion, + ## J. Assoc. Comput. Mach., 8 (1961), pp. 281-330. + ## J.H. Wilkinson, Rounding Errors in Algebraic Processes, Notes on Applied + ## Science No. 32, Her Majesty's Stationery Office, London, 1963. + ## J.H. Wilkinson, The Algebraic Eigenvalue Problem, Oxford University + ## Press, 1965. if (nargin != 1) error ("gallery: 1 argument is required for wilk matrix."); @@ -2807,21 +2807,21 @@ ## NOTE: bandred is part of the Test Matrix Toolbox and is used by randsvd() function A = bandred (A, kl, ku) ## BANDRED Band reduction by two-sided unitary transformations. - ## B = BANDRED(A, KL, KU) is a matrix unitarily equivalent to A - ## with lower bandwidth KL and upper bandwidth KU - ## (i.e. B(i,j) = 0 if i > j+KL or j > i+KU). - ## The reduction is performed using Householder transformations. - ## If KU is omitted it defaults to KL. + ## B = BANDRED(A, KL, KU) is a matrix unitarily equivalent to A + ## with lower bandwidth KL and upper bandwidth KU + ## (i.e. B(i,j) = 0 if i > j+KL or j > i+KU). + ## The reduction is performed using Householder transformations. + ## If KU is omitted it defaults to KL. ## - ## Called by RANDSVD. - ## This is a `standard' reduction. Cf. reduction to bidiagonal form - ## prior to computing the SVD. This code is a little wasteful in that - ## it computes certain elements which are immediately set to zero! + ## Called by RANDSVD. + ## This is a `standard' reduction. Cf. reduction to bidiagonal form + ## prior to computing the SVD. This code is a little wasteful in that + ## it computes certain elements which are immediately set to zero! ## - ## Reference: - ## G.H. Golub and C.F. Van Loan, Matrix Computations, second edition, - ## Johns Hopkins University Press, Baltimore, Maryland, 1989. - ## Section 5.4.3. + ## Reference: + ## G.H. Golub and C.F. Van Loan, Matrix Computations, second edition, + ## Johns Hopkins University Press, Baltimore, Maryland, 1989. + ## Section 5.4.3. ## Check for special case where order of left/right transformations matters. ## Easiest approach is to work on the transpose, flipping back at the end.