float.float32 #

fn arrays_tolerance(data1 []f32, data2 []f32, tol f32) bool

fn axpy_inc(alpha f32, x []f32, mut y []f32, n u32, incX u32, incY u32, ix u32, iy u32)

axpy_inc

fn axpy_inc_to(mut dst []f32, incdst u32, idst u32, alpha f32, x []f32, y []f32, n u32, incX u32, incY u32, ix u32, iy u32)

axpy_inc_to

fn axpy_unitary(alpha f32, x []f32, mut y []f32)

axpy_unitary

fn axpy_unitary_to(mut dst []f32, alpha f32, x []f32, y []f32)

axpy_unitary_to

fn dot_inc(x []f32, y []f32, n u32, incX u32, incY u32, ix u32, iy u32) f32

dot_inc

fn dot_unitary(x []f32, y []f32) f32

dot_unitary

fn gemv_n(m u32, n u32, alpha f32, a []f32, lda u32, x []f32, incx u32, beta f32, mut y []f32, incy u32)

gemv_n computes y = alpha * A * x + beta * y where A is an m×n dense matrix, x and y are vectors, and alpha and beta are scalars.

fn gemv_t(m u32, n u32, alpha f32, a []f32, lda u32, x []f32, incx u32, beta f32, mut y []f32, incy u32)

gemv_t computes y = alpha * Aᵀ * x + beta * y where A is an m×n dense matrix, x and y are vectors, and alpha and beta are scalars.

fn ger(m u32, n u32, alpha f32, x []f32, incx u32, y []f32, incy u32, mut a []f32, lda u32)

ger performs the rank-one operation A += alpha * x * yᵀ where A is an m×n dense matrix, x and y are vectors, and alpha is a scalar.

fn l2_distance_unitary(x []f32, y []f32) f32

l2_distance_unitary returns the L2-norm of x-y.

fn l2_norm_inc(x []f32, n u32, incx u32) f32

l2_norm_inc returns the L2-norm of x.

fn l2_norm_unitary(x []f32) f32

l2_norm_unitary returns the L2-norm of x.

fn scal_inc(alpha f32, mut x []f32, n u32, incx u32)

scal_inc

fn scal_inc_to(mut dst []f32, incdst u32, alpha f32, x []f32, n u32, incx u32)

scal_inc_to

fn scal_unitary(alpha f32, mut x []f32)

scal_unitary

fn scal_unitary_to(mut dst []f32, alpha f32, x []f32)

scal_unitary_to