Interface for Linear Solvers

The linear solver ls must be a subtype of the abstract type AbstractLinearSolver. It is then called as follows

Required methodsBrief description
ls(J, rhs; a₀ = 0, a₁ = 1, kwargs...)Compute the solution sol of the linear problem (a₀ * I + a₁ * J) * sol = rhs where J is the jacobian. Returns (sol, success::Bool, itnumber) where itnumber is the number of iterations for solving the problem.
Shifts

Two methods _axpy and _axpy_op are provided to help implementing this shift a₀ * I + a₁ * J

Interface for Eigen Solvers

The linear solver eig must be a subtype of the abstract type AbstractEigenSolver. It is then called as follows

Required methodsBrief description
eig(J, nev::Int; kwargs...)Compute the nev eigen-elements with largest real part. Returns (eigenvalues, eigenvectors, success:Bool, itnumber) where itnumber is the number of iterations for completing the computation. kwargs can be used to send information about the algorithm (perform bisection,...).
geteigenvector(eig, eigenvectors, i::Int)Returns the ith eigenvectors from the set of eigenvectors.

Interface for Bordered Linear Solvers

The bordered linear solver bls must be a subtype of the abstract type AbstractBorderedLinearSolver (which is itself a subtype of AbstractLinearSolver). It is used to solve

$$$\tag{BLS}\left[\begin{array}{ll} {\text{shift}\cdot I+J} & {dR} \\ {(\xi_u \cdot dz_u)^T} & {\xi_p \cdot dz_p} \end{array}\right] \cdot\left[\begin{array}{l} {dX} \\ {dl} \end{array}\right] = \left[\begin{array}{l} R \\ n \end{array}\right]$$$

where $\xi_u,\xi_p\in\mathbb C$ and $dR,\xi_u\in\mathbb C^N$.

Required methodsBrief description
bls(J, dR, dzu, dzp, R, n, ξu::Number, ξp::Number; shift = nothing, kwargs...)Compute the solution dX, dl of the linear problem (BLS) where J is the jacobian and dR, dzu are vectors (not necessarily subtypes of AbstractVector). shift = nothing is used in place of saying shift=0. Returns (dX, dl, success::Bool, itnumber) where itnumber is the number of iterations for solving the problem.