# Linear problems

Linear systems are of the form A*x=b where A should be a linear map that has the same type of output as input, i.e. the solution x should be of the same type as the right hand side b. They can be solved using the function linsolve:

KrylovKit.linsolveFunction
linsolve(A::AbstractMatrix, b::AbstractVector, [x₀, a₀::Number = 0, a₁::Number = 1];
kwargs...)
linsolve(f, b, [x₀, a₀::Number = 0, a₁::Number = 1]; kwargs...)
# expert version:
linsolve(f, b, x₀, algorithm, [a₀::Number = 0, a₁::Number = 1])

Compute a solution x to the linear system (a₀ + a₁ * A)*x = b or a₀ * x + a₁ * f(x) = b, possibly using a starting guess x₀. Return the approximate solution x and a ConvergenceInfo structure.

Arguments:

The linear map can be an AbstractMatrix (dense or sparse) or a general function or callable object. If no initial guess is specified, it is chosen as (zero(a₀)*zero(a₁))*b which should generate an object similar to b but initialized with zeros. The numbers a₀ and a₁ are optional arguments; they are applied implicitly, i.e. they do not contribute the computation time of applying the linear map or to the number of operations on vectors of type x and b.

Return values:

The return value is always of the form x, info = linsolve(...) with

• x: the approximate solution to the problem, similar type as the right hand side b but possibly with a different eltype

• info: an object of type [ConvergenceInfo], which has the following fields

• info.converged::Int: takes value 0 or 1 depending on whether the solution was converged up to the requested tolerance
• info.residual: residual b - f(x) of the approximate solution x
• info.normres::Real: norm of the residual, i.e. norm(info.residual)
• info.numops::Int: total number of times that the linear map was applied, i.e. the number of times that f was called, or a vector was multiplied with A
• info.numiter::Int: number of times the Krylov subspace was restarted (see below)
Check for convergence

No warning is printed if no converged solution was found, so always check if info.converged == 1.

Keyword arguments:

Keyword arguments are given by:

• verbosity::Int = 0: verbosity level, i.e. 0 (no messages), 1 (single message at the end), 2 (information after every iteration), 3 (information per Krylov step)
• atol::Real: the requested accuracy, i.e. absolute tolerance, on the norm of the residual.
• rtol::Real: the requested accuracy on the norm of the residual, relative to the norm of the right hand side b.
• tol::Real: the requested accuracy on the norm of the residual that is actually used by the algorithm; it defaults to max(atol, rtol*norm(b)). So either use atol and rtol or directly use tol (in which case the value of atol and rtol will be ignored).
• krylovdim::Integer: the maximum dimension of the Krylov subspace that will be constructed.
• maxiter::Integer: the number of times the Krylov subspace can be rebuilt; see below for further details on the algorithms.
• orth::Orthogonalizer: the orthogonalization method to be used, see Orthogonalizer
• issymmetric::Bool: if the linear map is symmetric, only meaningful if T<:Real
• ishermitian::Bool: if the linear map is hermitian
• isposdef::Bool: if the linear map is positive definite

The default values are given by atol = KrylovDefaults.tol, rtol = KrylovDefaults.tol, tol = max(atol, rtol*norm(b)), krylovdim = KrylovDefaults.krylovdim, maxiter = KrylovDefaults.maxiter, orth = KrylovDefaults.orth; see KrylovDefaults for details.

The default value for the last three parameters depends on the method. If an AbstractMatrix is used, issymmetric, ishermitian and isposdef are checked for that matrix, ortherwise the default values are issymmetric = false, ishermitian = T <: Real && issymmetric and isposdef = false.

Algorithms

The final (expert) method, without default values and keyword arguments, is the one that is finally called, and can also be used directly. Here, one specifies the algorithm explicitly. Currently, only CG and GMRES are implemented, where CG is chosen if isposdef == true. Note that in standard GMRES terminology, our parameter krylovdim is referred to as the restart parameter, and our maxiter parameter counts the number of outer iterations, i.e. restart cycles. In CG, the Krylov subspace is only implicit because short recurrence relations are being used, and therefore no restarts are required. Therefore, we pass krylovdim*maxiter as the maximal number of CG iterations that can be used by the CG algorithm.

source