## BiLQR

`Krylov.bilqr`

— Function```
(x, y, stats) = bilqr(A, b::AbstractVector{FC}, c::AbstractVector{FC};
transfer_to_bicg::Bool=true, atol::T=√eps(T),
rtol::T=√eps(T), itmax::Int=0,
timemax::Float64=Inf, verbose::Int=0, history::Bool=false,
callback=solver->false, iostream::IO=kstdout)
```

`T`

is an `AbstractFloat`

such as `Float32`

, `Float64`

or `BigFloat`

. `FC`

is `T`

or `Complex{T}`

.

`(x, y, stats) = bilqr(A, b, c, x0::AbstractVector, y0::AbstractVector; kwargs...)`

BiLQR can be warm-started from initial guesses `x0`

and `y0`

where `kwargs`

are the same keyword arguments as above.

Combine BiLQ and QMR to solve adjoint systems.

```
[0 A] [y] = [b]
[Aᴴ 0] [x] [c]
```

The relation `bᴴc ≠ 0`

must be satisfied. BiLQ is used for solving primal system `Ax = b`

of size n. QMR is used for solving dual system `Aᴴy = c`

of size n.

**Input arguments**

`A`

: a linear operator that models a matrix of dimension`n`

;`b`

: a vector of length`n`

;`c`

: a vector of length`n`

.

**Optional arguments**

`x0`

: a vector of length`n`

that represents an initial guess of the solution`x`

;`y0`

: a vector of length`n`

that represents an initial guess of the solution`y`

.

**Keyword arguments**

`transfer_to_bicg`

: transfer from the BiLQ point to the BiCG point, when it exists. The transfer is based on the residual norm;`atol`

: absolute stopping tolerance based on the residual norm;`rtol`

: relative stopping tolerance based on the residual norm;`itmax`

: the maximum number of iterations. If`itmax=0`

, the default number of iterations is set to`2n`

;`timemax`

: the time limit in seconds;`verbose`

: additional details can be displayed if verbose mode is enabled (verbose > 0). Information will be displayed every`verbose`

iterations;`history`

: collect additional statistics on the run such as residual norms, or Aᴴ-residual norms;`callback`

: function or functor called as`callback(solver)`

that returns`true`

if the Krylov method should terminate, and`false`

otherwise;`iostream`

: stream to which output is logged.

**Output arguments**

`x`

: a dense vector of length`n`

;`y`

: a dense vector of length`n`

;`stats`

: statistics collected on the run in an`AdjointStats`

structure.

**Reference**

- A. Montoison and D. Orban,
*BiLQ: An Iterative Method for Nonsymmetric Linear Systems with a Quasi-Minimum Error Property*, SIAM Journal on Matrix Analysis and Applications, 41(3), pp. 1145–1166, 2020.

`Krylov.bilqr!`

— Function```
solver = bilqr!(solver::BilqrSolver, A, b, c; kwargs...)
solver = bilqr!(solver::BilqrSolver, A, b, c, x0, y0; kwargs...)
```

where `kwargs`

are keyword arguments of `bilqr`

.

See `BilqrSolver`

for more details about the `solver`

.

## TriLQR

`Krylov.trilqr`

— Function```
(x, y, stats) = trilqr(A, b::AbstractVector{FC}, c::AbstractVector{FC};
transfer_to_usymcg::Bool=true, atol::T=√eps(T),
rtol::T=√eps(T), itmax::Int=0,
timemax::Float64=Inf, verbose::Int=0, history::Bool=false,
callback=solver->false, iostream::IO=kstdout)
```

`T`

is an `AbstractFloat`

such as `Float32`

, `Float64`

or `BigFloat`

. `FC`

is `T`

or `Complex{T}`

.

`(x, y, stats) = trilqr(A, b, c, x0::AbstractVector, y0::AbstractVector; kwargs...)`

TriLQR can be warm-started from initial guesses `x0`

and `y0`

where `kwargs`

are the same keyword arguments as above.

Combine USYMLQ and USYMQR to solve adjoint systems.

```
[0 A] [y] = [b]
[Aᴴ 0] [x] [c]
```

USYMLQ is used for solving primal system `Ax = b`

of size m × n. USYMQR is used for solving dual system `Aᴴy = c`

of size n × m.

**Input arguments**

`A`

: a linear operator that models a matrix of dimension`m × n`

;`b`

: a vector of length`m`

;`c`

: a vector of length`n`

.

**Optional arguments**

`x0`

: a vector of length`n`

that represents an initial guess of the solution`x`

;`y0`

: a vector of length`m`

that represents an initial guess of the solution`y`

.

**Keyword arguments**

`transfer_to_usymcg`

: transfer from the USYMLQ point to the USYMCG point, when it exists. The transfer is based on the residual norm;`atol`

: absolute stopping tolerance based on the residual norm;`rtol`

: relative stopping tolerance based on the residual norm;`itmax`

: the maximum number of iterations. If`itmax=0`

, the default number of iterations is set to`m+n`

;`timemax`

: the time limit in seconds;`verbose`

: additional details can be displayed if verbose mode is enabled (verbose > 0). Information will be displayed every`verbose`

iterations;`history`

: collect additional statistics on the run such as residual norms, or Aᴴ-residual norms;`callback`

: function or functor called as`callback(solver)`

that returns`true`

if the Krylov method should terminate, and`false`

otherwise;`iostream`

: stream to which output is logged.

**Output arguments**

`x`

: a dense vector of length`n`

;`y`

: a dense vector of length`m`

;`stats`

: statistics collected on the run in an`AdjointStats`

structure.

**Reference**

- A. Montoison and D. Orban,
*BiLQ: An Iterative Method for Nonsymmetric Linear Systems with a Quasi-Minimum Error Property*, SIAM Journal on Matrix Analysis and Applications, 41(3), pp. 1145–1166, 2020.

`Krylov.trilqr!`

— Function```
solver = trilqr!(solver::TrilqrSolver, A, b, c; kwargs...)
solver = trilqr!(solver::TrilqrSolver, A, b, c, x0, y0; kwargs...)
```

where `kwargs`

are keyword arguments of `trilqr`

.

See `TrilqrSolver`

for more details about the `solver`

.