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m testchol.m

% % testchol.m % % % Use the Cholesky decomposition decomposition % to solve a linear system with symmetric positive % definite matrix. % % A = [ 16 4 8 4 4 10 8 4 8 8 12 10
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m tridiag_ldlt.m

function [d, e, iflag] = tridiag_ldlt( d, e); % % TRIDIAG_LDLT computes the LDL^T-decomposition of a % symmetric positive definite tridiagonal matrix. % % Usage % [c, d, e, iflag] = tridiag_
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m lu_cp.m

function [A, ipivtr, ipivtc, iflag] = lu_pc( A ); % % LU_CP computes the LU--decomposition with complete % pivoting of a matrix A % % Usage % [A, ipivt, iflag] = lu_pp( A ) % % input: %
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m grdecord.m

function [Dec,Ord]=grDecOrd(E) % Function [Dec,Ord]=grDecOrd(E) solve % the problem about decomposition of the digraph % to the sections with mutually accessed vertexes % (strongly connected comp
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m neweig.m

function [T,Q] = neweig(A) % % Compute the eigenvalues and eigenvector of a real symmetric matrix A % % function [T,Q] = neweig(A) % % A = matrix whose eigendecomposition is sought % % T = di
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m tohankelbig.m

function [A,diff] = tohankelbig(A,r,d) % % Determine the matrix nearest to A which is (block) Hankel and has rank r % using the composite mapping algorithm % % function A = tohankelbig(A,r) %
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m rotqc2mc.m

function mc=rotqc2mc(qc) %ROTQC2MC converts a matrix of complex quaternion vectors to quaternion matrices % Inputs: % % QC(2m,n) mxn matrix of real quaternion vectors (each 2x1) % % Outpu
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m rotmr2qr.m

function qr=rotmr2qr(mr) %ROTMR2QR converts a matrix of real quaternion matrices to quaternion vectors % Inputs: % % MR(4m,4n) mxn matrix of real quaternion matrices (each 4x4) % % Output
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m rotmc2qc.m

function qc=rotmc2qc(mc) %ROTMC2QC converts a matrix of complex quaternion matrices to a matrix of complex quaternion vectors % Inputs: % % MC(2m,2n) mxn matrix of real quaternion matrices
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m rotqr2mr.m

function mr=rotqr2mr(qr) %ROTQR2MR converts a matrix of real quaternion vectors to quaternion matrices % Inputs: % % QR(4m,n) mxn matrix of real quaternion vectors (each 4x1) % % Outputs:
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