Library
Module
Module type
Parameter
Class
Class type
Double-precision complex BLAS and LAPACK functions.
This module Lacaml.Z
contains linear algebra routines for complex numbers (precision: complex64). It is recommended to use this module by writing
open Lacaml.Z
at the top of your file.
type prec = Bigarray.complex64_elt
type num_type = Complex.t
type vec =
(Complex.t, Bigarray.complex64_elt, Bigarray.fortran_layout)
Bigarray.Array1.t
Complex vectors (precision: complex64).
type rvec =
(float, Bigarray.float64_elt, Bigarray.fortran_layout) Bigarray.Array1.t
Vectors of reals (precision: float64).
type mat =
(Complex.t, Bigarray.complex64_elt, Bigarray.fortran_layout)
Bigarray.Array2.t
Complex matrices (precision: complex64).
val prec : (Complex.t, Bigarray.complex64_elt) Bigarray.kind
Precision for this submodule Z
. Allows to write precision independent code.
module Vec : sig ... end
module Mat : sig ... end
val pp_num : Format.formatter -> Complex.t -> unit
pp_num ppf el
is equivalent to fprintf ppf "(%G, %Gi)" el.re el.im
.
dotu ?n ?ofsx ?incx x ?ofsy ?incy y
see BLAS documentation!
dotc ?n ?ofsx ?incx x ?ofsy ?incy y
see BLAS documentation!
val lansy_min_lwork : int -> Common.norm4 -> int
lansy_min_lwork m norm
val lansy :
?n:int ->
?up:bool ->
?norm:Common.norm4 ->
?work:rvec ->
?ar:int ->
?ac:int ->
mat ->
float
lansy ?n ?up ?norm ?work ?ar ?ac a
see LAPACK documentation!
val gecon :
?n:int ->
?norm:Common.norm2 ->
?anorm:float ->
?work:vec ->
?rwork:rvec ->
?ar:int ->
?ac:int ->
mat ->
float
gecon ?n ?norm ?anorm ?work ?rwork ?ar ?ac a
val sycon :
?n:int ->
?up:bool ->
?ipiv:Common.int32_vec ->
?anorm:float ->
?work:vec ->
?ar:int ->
?ac:int ->
mat ->
float
sycon ?n ?up ?ipiv ?anorm ?work ?ar ?ac a
val pocon :
?n:int ->
?up:bool ->
?anorm:float ->
?work:vec ->
?rwork:rvec ->
?ar:int ->
?ac:int ->
mat ->
float
pocon ?n ?up ?anorm ?work ?rwork ?ar ?ac a
val gees :
?n:int ->
?jobvs:Common.schur_vectors ->
?sort:Common.eigen_value_sort ->
?w:vec ->
?vsr:int ->
?vsc:int ->
?vs:mat ->
?work:vec ->
?ar:int ->
?ac:int ->
mat ->
int * vec * mat
gees ?n ?jobvs ?sort ?w ?vsr ?vsc ?vs ?work ?ar ?ac a
See gees
-function for details about arguments.
val gesvd_opt_lwork :
?m:int ->
?n:int ->
?jobu:Common.svd_job ->
?jobvt:Common.svd_job ->
?s:rvec ->
?ur:int ->
?uc:int ->
?u:mat ->
?vtr:int ->
?vtc:int ->
?vt:mat ->
?ar:int ->
?ac:int ->
mat ->
int
val geev_opt_lwork :
?n:int ->
?vlr:int ->
?vlc:int ->
?vl:mat option ->
?vrr:int ->
?vrc:int ->
?vr:mat option ->
?ofsw:int ->
?w:vec ->
?ar:int ->
?ac:int ->
mat ->
int
geev ?work ?rwork ?n ?vlr ?vlc ?vl ?vrr ?vrc ?vr ?ofsw w ?ar ?ac a
See geev
-function for details about arguments.
val geev :
?n:int ->
?work:vec ->
?rwork:vec ->
?vlr:int ->
?vlc:int ->
?vl:mat option ->
?vrr:int ->
?vrc:int ->
?vr:mat option ->
?ofsw:int ->
?w:vec ->
?ar:int ->
?ac:int ->
mat ->
mat * vec * mat
geev ?work ?rwork ?n ?vlr ?vlc ?vl ?vrr ?vrc ?vr ?ofsw w ?ar ?ac a
swap ?n ?ofsx ?incx x ?ofsy ?incy y
see BLAS documentation!
scal ?n alpha ?ofsx ?incx x
see BLAS documentation!
copy ?n ?ofsy ?incy ?y ?ofsx ?incx x
see BLAS documentation!
val nrm2 : ?n:int -> ?ofsx:int -> ?incx:int -> vec -> float
nrm2 ?n ?ofsx ?incx x
see BLAS documentation!
val axpy :
?alpha:Complex.t ->
?n:int ->
?ofsx:int ->
?incx:int ->
vec ->
?ofsy:int ->
?incy:int ->
vec ->
unit
axpy ?alpha ?n ?ofsx ?incx x ?ofsy ?incy y
see BLAS documentation!
val iamax : ?n:int -> ?ofsx:int -> ?incx:int -> vec -> int
iamax ?n ?ofsx ?incx x
see BLAS documentation!
val gemv :
?m:int ->
?n:int ->
?beta:Complex.t ->
?ofsy:int ->
?incy:int ->
?y:vec ->
?trans:trans3 ->
?alpha:Complex.t ->
?ar:int ->
?ac:int ->
mat ->
?ofsx:int ->
?incx:int ->
vec ->
vec
gemv ?m ?n ?beta ?ofsy ?incy ?y ?trans ?alpha ?ar ?ac a ?ofsx ?incx x
performs the operation y
:= alpha
* op(a
) * x
+ beta
* y
where op(a
) = a
or a
ᵀ according to the value of trans
. See BLAS documentation for more information. BEWARE that the 1988 BLAS-2 specification mandates that this function has no effect when n=0
while the mathematically expected behavior is y ← beta * y
.
val gbmv :
?m:int ->
?n:int ->
?beta:Complex.t ->
?ofsy:int ->
?incy:int ->
?y:vec ->
?trans:trans3 ->
?alpha:Complex.t ->
?ar:int ->
?ac:int ->
mat ->
int ->
int ->
?ofsx:int ->
?incx:int ->
vec ->
vec
gbmv ?m ?n ?beta ?ofsy ?incy ?y ?trans ?alpha ?ar ?ac a kl ku ?ofsx ?incx
x
see BLAS documentation!
val symv :
?n:int ->
?beta:Complex.t ->
?ofsy:int ->
?incy:int ->
?y:vec ->
?up:bool ->
?alpha:Complex.t ->
?ar:int ->
?ac:int ->
mat ->
?ofsx:int ->
?incx:int ->
vec ->
vec
symv ?n ?beta ?ofsy ?incy ?y ?up ?alpha ?ar ?ac a ?ofsx ?incx x
see BLAS documentation!
val trmv :
?n:int ->
?trans:trans3 ->
?diag:Common.diag ->
?up:bool ->
?ar:int ->
?ac:int ->
mat ->
?ofsx:int ->
?incx:int ->
vec ->
unit
trmv ?n ?trans ?diag ?up ?ar ?ac a ?ofsx ?incx x
see BLAS documentation!
val trsv :
?n:int ->
?trans:trans3 ->
?diag:Common.diag ->
?up:bool ->
?ar:int ->
?ac:int ->
mat ->
?ofsx:int ->
?incx:int ->
vec ->
unit
trsv ?n ?trans ?diag ?up ?ar ?ac a ?ofsx ?incx x
see BLAS documentation!
val tpmv :
?n:int ->
?trans:trans3 ->
?diag:Common.diag ->
?up:bool ->
?ofsap:int ->
vec ->
?ofsx:int ->
?incx:int ->
vec ->
unit
tpmv ?n ?trans ?diag ?up ?ofsap ap ?ofsx ?incx x
see BLAS documentation!
val tpsv :
?n:int ->
?trans:trans3 ->
?diag:Common.diag ->
?up:bool ->
?ofsap:int ->
vec ->
?ofsx:int ->
?incx:int ->
vec ->
unit
tpsv ?n ?trans ?diag ?up ?ofsap ap ?ofsx ?incx x
see BLAS documentation!
val gemm :
?m:int ->
?n:int ->
?k:int ->
?beta:Complex.t ->
?cr:int ->
?cc:int ->
?c:mat ->
?transa:trans3 ->
?alpha:Complex.t ->
?ar:int ->
?ac:int ->
mat ->
?transb:trans3 ->
?br:int ->
?bc:int ->
mat ->
mat
gemm ?m ?n ?k ?beta ?cr ?cc ?c ?transa ?alpha ?ar ?ac a ?transb ?br ?bc b
performs the operation c
:= alpha
* op(a
) * op(b
) + beta
* c
where op(x
) = x
or x
ᵀ depending on transx
. See BLAS documentation for more information.
val symm :
?m:int ->
?n:int ->
?side:Common.side ->
?up:bool ->
?beta:Complex.t ->
?cr:int ->
?cc:int ->
?c:mat ->
?alpha:Complex.t ->
?ar:int ->
?ac:int ->
mat ->
?br:int ->
?bc:int ->
mat ->
mat
symm ?m ?n ?side ?up ?beta ?cr ?cc ?c ?alpha ?ar ?ac a ?br ?bc b
see BLAS documentation!
val trmm :
?m:int ->
?n:int ->
?side:Common.side ->
?up:bool ->
?transa:trans3 ->
?diag:Common.diag ->
?alpha:Complex.t ->
?ar:int ->
?ac:int ->
mat ->
?br:int ->
?bc:int ->
mat ->
unit
trmm ?m ?n ?side ?up ?transa ?diag ?alpha ?ar ?ac a ?br ?bc b
see BLAS documentation!
val trsm :
?m:int ->
?n:int ->
?side:Common.side ->
?up:bool ->
?transa:trans3 ->
?diag:Common.diag ->
?alpha:Complex.t ->
?ar:int ->
?ac:int ->
mat ->
?br:int ->
?bc:int ->
mat ->
unit
trsm ?m ?n ?side ?up ?transa ?diag ?alpha ?ar ?ac ~a ?br ?bc b
see BLAS documentation!
val syrk :
?n:int ->
?k:int ->
?up:bool ->
?beta:Complex.t ->
?cr:int ->
?cc:int ->
?c:mat ->
?trans:Common.trans2 ->
?alpha:Complex.t ->
?ar:int ->
?ac:int ->
mat ->
mat
syrk ?n ?k ?up ?beta ?cr ?cc ?c ?trans ?alpha ?ar ?ac a
see BLAS documentation!
val syr2k :
?n:int ->
?k:int ->
?up:bool ->
?beta:Complex.t ->
?cr:int ->
?cc:int ->
?c:mat ->
?trans:Common.trans2 ->
?alpha:Complex.t ->
?ar:int ->
?ac:int ->
mat ->
?br:int ->
?bc:int ->
mat ->
mat
syr2k ?n ?k ?up ?beta ?cr ?cc ?c ?trans ?alpha ?ar ?ac a ?br ?bc b
see BLAS documentation!
val lacpy :
?uplo:[ `U | `L ] ->
?patt:Common.Types.Mat.patt ->
?m:int ->
?n:int ->
?br:int ->
?bc:int ->
?b:mat ->
?ar:int ->
?ac:int ->
mat ->
mat
lacpy ?patt ?uplo ?m ?n ?br ?bc ?b ?ar ?ac a
copy the (triangular) (sub-)matrix a
(to an optional (sub-)matrix b
) and return it. patt
is more general than uplo
and should be used in its place whenever strict BLAS conformance is not required. Only one of patt
and uplo
can be specified at a time.
val laswp :
?n:int ->
?ar:int ->
?ac:int ->
mat ->
?k1:int ->
?k2:int ->
?incx:int ->
Common.int32_vec ->
unit
laswp ?n ?ar ?ac a ?k1 ?k2 ?incx ipiv
swap rows of a
according to ipiv
. See LAPACK-documentation for details!
val lapmt :
?forward:bool ->
?m:int ->
?n:int ->
?ar:int ->
?ac:int ->
mat ->
Common.int32_vec ->
unit
lapmt ?forward ?n ?m ?ar ?ac a k
swap columns of a
according to the permutations in k
. See LAPACK-documentation for details!
val lassq :
?n:int ->
?scale:float ->
?sumsq:float ->
?ofsx:int ->
?incx:int ->
vec ->
float * float
lassq ?n ?ofsx ?incx ?scale ?sumsq
val larnv :
?idist:[ `Uniform0 | `Uniform1 | `Normal ] ->
?iseed:Common.int32_vec ->
?n:int ->
?ofsx:int ->
?x:vec ->
unit ->
vec
larnv ?idist ?iseed ?n ?ofsx ?x ()
val lange_min_lwork : int -> Common.norm4 -> int
lange_min_lwork m norm
val lange :
?m:int ->
?n:int ->
?norm:Common.norm4 ->
?work:rvec ->
?ar:int ->
?ac:int ->
mat ->
float
lange ?m ?n ?norm ?work ?ar ?ac a
val lauum : ?n:int -> ?up:bool -> ?ar:int -> ?ac:int -> mat -> unit
lauum ?n ?up ?ar ?ac a
computes the product U * U**T or L**T * L, where the triangular factor U or L is stored in the upper or lower triangular part of the array a
. The upper or lower part of a
is overwritten.
val getrf :
?m:int ->
?n:int ->
?ipiv:Common.int32_vec ->
?ar:int ->
?ac:int ->
mat ->
Common.int32_vec
getrf ?m ?n ?ipiv ?ar ?ac a
computes an LU factorization of a general m
-by-n
matrix a
using partial pivoting with row interchanges. See LAPACK documentation.
val getrs :
?n:int ->
?ipiv:Common.int32_vec ->
?trans:trans3 ->
?ar:int ->
?ac:int ->
mat ->
?nrhs:int ->
?br:int ->
?bc:int ->
mat ->
unit
val getri_opt_lwork : ?n:int -> ?ar:int -> ?ac:int -> mat -> int
getri_opt_lwork ?n ?ar ?ac a
val getri :
?n:int ->
?ipiv:Common.int32_vec ->
?work:vec ->
?ar:int ->
?ac:int ->
mat ->
unit
val sytrf_opt_lwork : ?n:int -> ?up:bool -> ?ar:int -> ?ac:int -> mat -> int
sytrf_opt_lwork ?n ?up ?ar ?ac a
val sytrf :
?n:int ->
?up:bool ->
?ipiv:Common.int32_vec ->
?work:vec ->
?ar:int ->
?ac:int ->
mat ->
Common.int32_vec
sytrf ?n ?up ?ipiv ?work ?ar ?ac a
computes the factorization of the real symmetric matrix a
using the Bunch-Kaufman diagonal pivoting method.
val sytrs :
?n:int ->
?up:bool ->
?ipiv:Common.int32_vec ->
?ar:int ->
?ac:int ->
mat ->
?nrhs:int ->
?br:int ->
?bc:int ->
mat ->
unit
val sytri :
?n:int ->
?up:bool ->
?ipiv:Common.int32_vec ->
?work:vec ->
?ar:int ->
?ac:int ->
mat ->
unit
val potrf : ?n:int -> ?up:bool -> ?ar:int -> ?ac:int -> mat -> unit
potrf ?n ?up ?ar ?ac a
factorizes symmetric positive definite matrix a
(or the designated submatrix) using Cholesky factorization.
val potrs :
?n:int ->
?up:bool ->
?ar:int ->
?ac:int ->
mat ->
?nrhs:int ->
?br:int ->
?bc:int ->
mat ->
unit
potrs ?n ?up ?ar ?ac a ?nrhs ?br ?bc b
solves a system of linear equations a
*X = b
, where a
is symmetric positive definite matrix, using the Cholesky factorization a
= U**T*U or a
= L*L**T computed by potrf
.
val potri : ?n:int -> ?up:bool -> ?ar:int -> ?ac:int -> mat -> unit
potri ?n ?up ?ar ?ac a
computes the inverse of the real symmetric positive definite matrix a
using the Cholesky factorization a
= U**T*U or a
= L*L**T computed by potrf
.
val trtrs :
?n:int ->
?up:bool ->
?trans:trans3 ->
?diag:Common.diag ->
?ar:int ->
?ac:int ->
mat ->
?nrhs:int ->
?br:int ->
?bc:int ->
mat ->
unit
trtrs ?n ?up ?trans ?diag ?ar ?ac a ?nrhs ?br ?bc b
solves a triangular system of the form a
* X = b
or a
**T * X = n
, where a
is a triangular matrix of order n
, and b
is an n
-by-nrhs
matrix.
val tbtrs :
?n:int ->
?kd:int ->
?up:bool ->
?trans:trans3 ->
?diag:Common.diag ->
?abr:int ->
?abc:int ->
mat ->
?nrhs:int ->
?br:int ->
?bc:int ->
mat ->
unit
tbtrs ?n ?kd ?up ?trans ?diag ?abr ?abc ab ?nrhs ?br ?bc b
solves a triangular system of the form a
* X = b
or a
**T * X = b
, where a
is a triangular band matrix of order n
, and b
is an n
-by-nrhs
matrix.
val trtri :
?n:int ->
?up:bool ->
?diag:Common.diag ->
?ar:int ->
?ac:int ->
mat ->
unit
trtri ?n ?up ?diag ?ar ?ac a
computes the inverse of a real upper or lower triangular matrix a
.
val geqrf_opt_lwork : ?m:int -> ?n:int -> ?ar:int -> ?ac:int -> mat -> int
geqrf_opt_lwork ?m ?n ?ar ?ac a
geqrf ?m ?n ?work ?tau ?ar ?ac a
computes a QR factorization of a real m
-by-n
matrix a
. See LAPACK documentation.
val gesv :
?n:int ->
?ipiv:Common.int32_vec ->
?ar:int ->
?ac:int ->
mat ->
?nrhs:int ->
?br:int ->
?bc:int ->
mat ->
unit
gesv ?n ?ipiv ?ar ?ac a ?nrhs ?br ?bc b
computes the solution to a real system of linear equations a
* X = b
, where a
is an n
-by-n
matrix and X and b
are n
-by-nrhs
matrices. The LU decomposition with partial pivoting and row interchanges is used to factor a
as a
= P * L * U, where P is a permutation matrix, L is unit lower triangular, and U is upper triangular. The factored form of a
is then used to solve the system of equations a
* X = b
. On exit, b
contains the solution matrix X.
val gbsv :
?n:int ->
?ipiv:Common.int32_vec ->
?abr:int ->
?abc:int ->
mat ->
int ->
int ->
?nrhs:int ->
?br:int ->
?bc:int ->
mat ->
unit
gbsv ?n ?ipiv ?abr ?abc ab kl ku ?nrhs ?br ?bc b
computes the solution to a real system of linear equations a
* X = b
, where a
is a band matrix of order n
with kl
subdiagonals and ku
superdiagonals, and X and b
are n
-by-nrhs
matrices. The LU decomposition with partial pivoting and row interchanges is used to factor a
as a
= L * U, where L is a product of permutation and unit lower triangular matrices with kl
subdiagonals, and U is upper triangular with kl+ku
superdiagonals. The factored form of a
is then used to solve the system of equations a
* X = b
.
val gtsv :
?n:int ->
?ofsdl:int ->
vec ->
?ofsd:int ->
vec ->
?ofsdu:int ->
vec ->
?nrhs:int ->
?br:int ->
?bc:int ->
mat ->
unit
gtsv ?n ?ofsdl dl ?ofsd d ?ofsdu du ?nrhs ?br ?bc b
solves the equation a
* X = b
where a
is an n
-by-n
tridiagonal matrix, by Gaussian elimination with partial pivoting. Note that the equation A
'*X = b
may be solved by interchanging the order of the arguments du
and dl
.
val posv :
?n:int ->
?up:bool ->
?ar:int ->
?ac:int ->
mat ->
?nrhs:int ->
?br:int ->
?bc:int ->
mat ->
unit
posv ?n ?up ?ar ?ac a ?nrhs ?br ?bc b
computes the solution to a real system of linear equations a
* X = b
, where a
is an n
-by-n
symmetric positive definite matrix and X and b
are n
-by-nrhs
matrices. The Cholesky decomposition is used to factor a
as a
= U**T * U, if up = true
, or a
= L * L**T, if up = false
, where U is an upper triangular matrix and L is a lower triangular matrix. The factored form of a
is then used to solve the system of equations a
* X = b
.
val ppsv :
?n:int ->
?up:bool ->
?ofsap:int ->
vec ->
?nrhs:int ->
?br:int ->
?bc:int ->
mat ->
unit
ppsv ?n ?up ?ofsap ap ?nrhs ?br ?bc b
computes the solution to the real system of linear equations a
* X = b
, where a
is an n
-by-n
symmetric positive definite matrix stored in packed format and X and b
are n
-by-nrhs
matrices. The Cholesky decomposition is used to factor a
as a
= U**T * U, if up = true
, or a
= L * L**T, if up = false
, where U is an upper triangular matrix and L is a lower triangular matrix. The factored form of a
is then used to solve the system of equations a
* X = b
.
val pbsv :
?n:int ->
?up:bool ->
?kd:int ->
?abr:int ->
?abc:int ->
mat ->
?nrhs:int ->
?br:int ->
?bc:int ->
mat ->
unit
pbsv ?n ?up ?kd ?abr ?abc ab ?nrhs ?br ?bc b
computes the solution to a real system of linear equations a
* X = b
, where a
is an n
-by-n
symmetric positive definite band matrix and X and b
are n
-by-nrhs
matrices. The Cholesky decomposition is used to factor a
as a
= U**T * U, if up = true
, or a
= L * L**T, if up = false
, where U is an upper triangular band matrix, and L is a lower triangular band matrix, with the same number of superdiagonals or subdiagonals as a
. The factored form of a
is then used to solve the system of equations a
* X = b
.
val ptsv :
?n:int ->
?ofsd:int ->
rvec ->
?ofse:int ->
vec ->
?nrhs:int ->
?br:int ->
?bc:int ->
mat ->
unit
ptsv ?n ?ofsd d ?ofse e ?nrhs ?br ?bc b
computes the solution to the real system of linear equations a
*X = b
, where a
is an n
-by-n
symmetric positive definite tridiagonal matrix, and X and b
are n
-by-nrhs
matrices. A is factored as a
= L*D*L**T, and the factored form of a
is then used to solve the system of equations.
val sysv_opt_lwork :
?n:int ->
?up:bool ->
?ar:int ->
?ac:int ->
mat ->
?nrhs:int ->
?br:int ->
?bc:int ->
mat ->
int
sysv_opt_lwork ?n ?up ?ar ?ac a ?nrhs ?br ?bc b
val sysv :
?n:int ->
?up:bool ->
?ipiv:Common.int32_vec ->
?work:vec ->
?ar:int ->
?ac:int ->
mat ->
?nrhs:int ->
?br:int ->
?bc:int ->
mat ->
unit
sysv ?n ?up ?ipiv ?work ?ar ?ac a ?nrhs ?br ?bc b
computes the solution to a real system of linear equations a
* X = b
, where a
is an N-by-N symmetric matrix and X and b
are n
-by-nrhs
matrices. The diagonal pivoting method is used to factor a
as a
= U * D * U**T, if up = true
, or a
= L * D * L**T, if up = false
, where U (or L) is a product of permutation and unit upper (lower) triangular matrices, and D is symmetric and block diagonal with 1-by-1 and 2-by-2 diagonal blocks. The factored form of a
is then used to solve the system of equations a
* X = b
.
val spsv :
?n:int ->
?up:bool ->
?ipiv:Common.int32_vec ->
?ofsap:int ->
vec ->
?nrhs:int ->
?br:int ->
?bc:int ->
mat ->
unit
spsv ?n ?up ?ipiv ?ofsap ap ?nrhs ?br ?bc b
computes the solution to the real system of linear equations a
* X = b
, where a
is an n
-by-n
symmetric matrix stored in packed format and X and b
are n
-by-nrhs
matrices. The diagonal pivoting method is used to factor a
as a
= U * D * U**T, if up = true
, or a
= L * D * L**T, if up = false
, where U (or L) is a product of permutation and unit upper (lower) triangular matrices, D is symmetric and block diagonal with 1-by-1 and 2-by-2 diagonal blocks. The factored form of a
is then used to solve the system of equations a
* X = b
.
val gels_opt_lwork :
?m:int ->
?n:int ->
?trans:Common.trans2 ->
?ar:int ->
?ac:int ->
mat ->
?nrhs:int ->
?br:int ->
?bc:int ->
mat ->
int
gels_opt_lwork ?m ?n ?trans ?ar ?ac a ?nrhs ?br ?bc b
val gels :
?m:int ->
?n:int ->
?work:vec ->
?trans:Common.trans2 ->
?ar:int ->
?ac:int ->
mat ->
?nrhs:int ->
?br:int ->
?bc:int ->
mat ->
unit
gels ?m ?n ?work ?trans ?ar ?ac a ?nrhs ?br ?bc b
see LAPACK documentation!