Locally optimal block preconditioned conjugate gradient (LOBPCG)

Solves the generalized eigenproblem $Ax = λBx$ approximately where $A$ and $B$ are Hermitian linear maps, and $B$ is positive definite. $B$ is taken to be the identity by default. It can find the smallest (or largest) k eigenvalues and their corresponding eigenvectors which are B-orthonormal. It also admits a preconditioner and a "constraints" matrix C, such that the algorithm returns the smallest (or largest) eigenvalues associated with the eigenvectors in the nullspace of C'B.

Usage

IterativeSolvers.lobpcg — Function

The Locally Optimal Block Preconditioned Conjugate Gradient Method (LOBPCG)

Finds the nev extremal eigenvalues and their corresponding eigenvectors satisfying AX = λBX.

A and B may be generic types but Base.mul!(C, AorB, X) must be defined for vectors and strided matrices X and C. size(A, i::Int) and eltype(A) must also be defined for A.

lobpcg(A, [B,] largest, nev; kwargs...) -> results

Arguments

A: linear operator;
B: linear operator;
largest: true if largest eigenvalues are desired and false if smallest;
nev: number of eigenvalues desired.

Keywords

log::Bool: default is false; if true, results.trace will store iterations states; if false only results.trace will be empty;
P: preconditioner of residual vectors, must overload ldiv!;
C: constraint to deflate the residual and solution vectors orthogonal to a subspace; must overload mul!;
maxiter: maximum number of iterations; default is 200;
tol::Real: tolerance to which residual vector norms must be under.

Output

results: a LOBPCGResults struct. r.λ and r.X store the eigenvalues and eigenvectors.

source

lobpcg(A, [B,] largest, X0; kwargs...) -> results

Arguments

A: linear operator;
B: linear operator;
largest: true if largest eigenvalues are desired and false if smallest;
X0: Initial guess, will not be modified. The number of columns is the number of eigenvectors desired.

Keywords

not_zeros: default is false. If true, X0 will be assumed to not have any all-zeros column.
log::Bool: default is false; if true, results.trace will store iterations states; if false only results.trace will be empty;
P: preconditioner of residual vectors, must overload ldiv!;
C: constraint to deflate the residual and solution vectors orthogonal to a subspace; must overload mul!;
maxiter: maximum number of iterations; default is 200;
tol::Real: tolerance to which residual vector norms must be under.

Output

results: a LOBPCGResults struct. r.λ and r.X store the eigenvalues and eigenvectors.

source

lobpcg(A, [B,] largest, X0, nev; kwargs...) -> results

Arguments

A: linear operator;
B: linear operator;
largest: true if largest eigenvalues are desired and false if smallest;
X0: block vectors such that the eigenvalues will be found size(X0, 2) at a time; the columns are also used to initialize the first batch of Ritz vectors;
nev: number of eigenvalues desired.

Keywords

log::Bool: default is false; if true, results.trace will store iterations states; if false only results.trace will be empty;
P: preconditioner of residual vectors, must overload ldiv!;
C: constraint to deflate the residual and solution vectors orthogonal to a subspace; must overload mul!;
maxiter: maximum number of iterations; default is 200;
tol::Real: tolerance to which residual vector norms must be under.

Output

results: a LOBPCGResults struct. r.λ and r.X store the eigenvalues and eigenvectors.

source

IterativeSolvers.lobpcg! — Function

lobpcg!(iterator::LOBPCGIterator; kwargs...) -> results

Arguments

iterator::LOBPCGIterator: a struct having all the variables required for the LOBPCG algorithm.

Keywords

not_zeros: default is false. If true, the initial Ritz vectors will be assumed to not have any all-zeros column.
log::Bool: default is false; if true, results.trace will store iterations states; if false only results.trace will be empty;
maxiter: maximum number of iterations; default is 200;
tol::Real: tolerance to which residual vector norms must be under.

Output

results: a LOBPCGResults struct. r.λ and r.X store the eigenvalues and eigenvectors.

source

Implementation Details

A LOBPCGIterator is created to pre-allocate all the memory required by the method using the constructor LOBPCGIterator(A, B, largest, X, P, C) where A and B are the matrices from the generalized eigenvalue problem, largest indicates if the problem is a maximum or minimum eigenvalue problem, X is the initial eigenbasis, randomly sampled if not input, where size(X, 2) is the block size bs. P is the preconditioner, nothing by default, and C is the constraints matrix. The desired k eigenvalues are found bs at a time.

References

Implementation is based on ^{[Knyazev1993]} and ^[Scipy].

Knyazev1993Andrew V. Knyazev. "Toward the Optimal Preconditioned Eigensolver: Locally Optimal Block Preconditioned Conjugate Gradient Method" SIAM Journal on Scientific Computing, 23(2):517–541 2001.
ScipySee Scipy LOBPCG implementation