Golub-Kahan-Lanczos (SVDL)
The SVDL method computes a partial, approximate SVD decomposition of a general linear operator $A$.
Usage
IterativeSolvers.svdl — Functionsvdl(A) -> Σ, L, [history]Compute some singular values (and optionally vectors) using Golub-Kahan-Lanczos bidiagonalization [Golub1965] with thick restarting [Wu2000].
If log is set to true is given, method will output a tuple X, L, ch. Where ch is a ConvergenceHistory object. Otherwise it will only return X, L.
Arguments
A: The matrix or matrix-like object whose singular values are desired.
Keywords
nsv::Int = 6: number of singular values requested;v0 = random unit vector: starting guess vector in the domain ofA. The length ofqshould be the number of columns inA;k::Int = 2nsv: maximum number of Lanczos vectors to compute before restarting;j::Int = nsv: number of vectors to keep at the end of the restart. We don't recommend j < nsv;maxiter::Int = minimum(size(A)): maximum number of iterations to run;verbose::Bool = false: print information at each iteration;tol::Real = √eps(): maximum absolute error in each desired singular value;reltol::Real=√eps(): maximum error in each desired singular value relative to the estimated norm of the input matrix;method::Symbol=:ritz: restarting algorithm to use. Valid choices are::ritz: Thick restart with Ritz values [Wu2000].:harmonic: Restart with harmonic Ritz values [Baglama2005].
vecs::Symbol = :none: singular vectors to return.:both: Both left and right singular vectors are returned.:left: Only the left singular vectors are returned.:right: Only the right singular vectors are returned.:none: No singular vectors are returned.
dolock::Bool=false: Iftrue, locks converged Ritz values, removing them from the Krylov subspace being searched in the next macroiteration;log::Bool = false: output an extra element of typeConvergenceHistorycontaining extra information of the method execution.
Return values
if log is false
Σ: list of the desired singular values ifvecs == :none(the default), otherwise returns anSVDobject with the desired singular vectors filled in;L: computed partial factorizations of A.
if log is true
Σ: list of the desired singular values ifvecs == :none(the default),
otherwise returns an SVD object with the desired singular vectors filled in;
L: computed partial factorizations of A;history: convergence history.
ConvergenceHistory keys
:betas=>betas: The history of the computed betas.:Bs=>Bs: The history of the computed projected matrices.:ritz=>ritzvalhist: Ritz values computed at each iteration.:conv=>convhist: Convergence data.
Implementation details
The implementation of thick restarting follows closely that of SLEPc as described in [Hernandez2008]. Thick restarting can be turned off by setting k = maxiter, but most of the time this is not desirable.
The singular vectors are computed directly by forming the Ritz vectors from the product of the Lanczos vectors L.P/L.Q and the singular vectors of L.B. Additional accuracy in the singular triples can be obtained using inverse iteration.
- Golub1965Golub, Gene, and William Kahan. "Calculating the singular values and pseudo-inverse of a matrix." Journal of the Society for Industrial and Applied Mathematics, Series B: Numerical Analysis 2.2 (1965): 205-224.
- Wu2000Wu, Kesheng, and Horst Simon. "Thick-restart Lanczos method for large symmetric eigenvalue problems." SIAM Journal on Matrix Analysis and Applications 22.2 (2000): 602-616.
- Hernandez2008Vicente Hernández, José E. Román, and Andrés Tomás. "A robust and efficient parallel SVD solver based on restarted Lanczos bidiagonalization." Electronic Transactions on Numerical Analysis 31 (2008): 68-85.