Input matrix is badly conditioned. It may contain Inf or NaN. Specify either u0 field or v0 field in the options structure, but not both. Function handles only supported for mode ''largest''. First input must be either a double matrix or a function handle. Unexpected output of function handle with flag ''{0}''. The expected output is a column vector of length {1} and of type double. The number of singular values to compute must be a positive integer. maxit field in the options structure must be a positive integer. When using function handles, the second input must be a 1-by-2 vector of integers representing the matrix size. p field in the options structure must be a nonnegative integer. Scalar shift SIGMA can be ''largest'', ''smallest'', smallestnz'', or a finite real double. tol field in the options structure must be a nonnegative real number. Invalid starting vector. u0 field in the options structure must be a column vector with length equal to the number of rows in the input matrix. v0 field in the options structure must be a column vector with length equal to the number of columns in the input matrix. disp field in the options structure must be 0 or 1. p field in the options structure must be greater than or equal to K + 2, where K is the number of singular values. Parameter name must be ''Tolerance'', ''MaxIterations'', ''SubspaceDimension'', ''LeftStartVector'', ''RightStartVector'', ''Display'', or ''FailureTreatment''. Incorrect number of input arguments. Each parameter name must be followed by a corresponding value. Specify either ''LeftStartVector'' parameter or ''RightStartVector'' parameter, but not both. ''MaxIterations'' value must be a positive integer. ''SubspaceDimension'' value must be a nonnegative integer. ''Tolerance'' value must be a nonnegative real number. ''LeftStartVector'' value must be a numeric vector with length equal to the number of rows in the input matrix. ''RightStartVector'' value must be a numeric vector with length equal to the number of columns in the input matrix. ''Display'' value must be 0 or 1. ''SubspaceDimension'' value must be greater than or equal to K + 2, where K is the number of singular values. fail field in the options structure must be ''replacenan'', ''keep'', or ''drop''. ''FailureTreatment'' value must be ''replacenan'', ''keep'', or ''drop''. For numeric scalar shift, value of fail field in the options structure must be ''drop''. For numeric scalar shift, ''FailureTreatment'' value must be ''drop''. Only {0,number,integer} of the {1,number,integer} requested singular values converged. Singular values that did not converge are NaN. Only {0,number,integer} of the {1,number,integer} requested singular values converged. Specify convergence flag output to determine the values that did not converge. Only {0,number,integer} of the {1,number,integer} requested singular values converged. Singular values that did not converge are removed. Results might be inaccurate. To compute the singular values closest to 0, use option ''smallest'' instead. Large residual norm detected. This is likely due to bad condition of the input matrix (condition number {0}). Returned singular values might not be the largest. Increase ''MaxIterations'' value. Returned singular values might not be the smallest. Increase ''MaxIterations'' value. Only {0} singular values found. Try increasing the scalar shift SIGMA. Third argument must be a real scalar or the character ''L''. Options must be a structure. Singular value decomposition A*v = sigma*u, A'*u = sigma*v Computing {0} largest singular values of {1}-by-{2} matrix A. Computing {0} smallest singular values of {1}-by-{2} matrix A. Computing {0} smallest non-zero singular values of {1}-by-{2} matrix A. Computing {0} singular values closest to {3} of {1}-by-{2} matrix A. Parameters:\n Maximum number of iterations: {0}\n Tolerance: {1}\n Subspace Dimension: {2} Compute SVDS by calling SVD, because the subspace dimension is equal to the minimum matrix size. Compute SVDS by calling SVD, because the subspace dimension is larger than or equal to the size of matrix R. Find largest singular values for A*v = sigma*u, A'*u = sigma * v. Find largest singular values for R\\v = sigma*u, R'\\u = sigma * v, with A = Q*R*P.\nCompute QR decomposition of A... Find largest singular values for R\\v = sigma*u, R'\\u = sigma * v, with A' = Q*R*P.\nCompute QR decomposition of A'... Find eigenvalues closest to {0} of matrix [0 A; A' 0]*x = lambda*x. Call EIGS to compute eigenvalues... Matrix A has rank {0}, therefore all {1} requested singular values are zero. Matrix A has rank {0}, therefore {1} of the {2} requested singular values are zero. Matrix A has rank {0}, therefore only {1} instead of the requested {2} non-zero singular values can be returned. Bad condition suspected. Check residuals using A instead of matrix R from QR decomposition. Maximum relative residual: {0} Start of Lanczos bidiagonalization method Iteration {0}: {1} of {2} singular values converged. Smallest non-converged residual {3} (tolerance {4}). Iteration {0}: {1} of {2} singular values converged. To check if singular value multiplicities were missed, restart the method, looking for k+1 singular values. No additional multiple singular values found. Successful return. Some additional multiple singular values were found. Restart again to check for more. Found {0} candidates for singular values: {1} are positive and {2} are approximately zero. Check residuals of singular value candidates: All accepted. Check residuals of singular value candidates: {0} not accurate enough (greater than or equal to {1}).