lib.rs

//! ndarray-free safe Rust wrapper for LAPACK FFI
//!
//! `Lapack` trait and sub-traits
//! -------------------------------
//!
//! This crates provides LAPACK wrapper as `impl` of traits to base scalar types.
//! For example, LU decomposition to double-precision matrix is provided like:
//!
//! ```ignore
//! impl Solve_ for f64 {
//!     fn lu(l: MatrixLayout, a: &mut [Self]) -> Result<Pivot> { ... }
//! }
//! ```
//!
//! see [Solve_] for detail. You can use it like `f64::lu`:
//!
//! ```
//! use lax::{Solve_, layout::MatrixLayout, Transpose};
//!
//! let mut a = vec![
//!   1.0, 2.0,
//!   3.0, 4.0
//! ];
//! let mut b = vec![1.0, 2.0];
//! let layout = MatrixLayout::C { row: 2, lda: 2 };
//! let pivot = f64::lu(layout, &mut a).unwrap();
//! f64::solve(layout, Transpose::No, &a, &pivot, &mut b).unwrap();
//! ```
//!
//! When you want to write generic algorithm for real and complex matrices,
//! this trait can be used as a trait bound:
//!
//! ```
//! use lax::{Solve_, layout::MatrixLayout, Transpose};
//!
//! fn solve_at_once<T: Solve_>(layout: MatrixLayout, a: &mut [T], b: &mut [T]) -> Result<(), lax::error::Error> {
//!   let pivot = T::lu(layout, a)?;
//!   T::solve(layout, Transpose::No, a, &pivot, b)?;
//!   Ok(())
//! }
//! ```
//!
//! There are several similar traits as described below to keep development easy.
//! They are merged into a single trait, [Lapack].
//!
//! Linear equation, Inverse matrix, Condition number
//! --------------------------------------------------
//!
//! According to the property input metrix, several types of triangular decomposition are used:
//!
//! - [Solve_] trait provides methods for LU-decomposition for general matrix.
//! - [Solveh_] triat provides methods for Bunch-Kaufman diagonal pivoting method for symmetric/hermite indefinite matrix.
//! - [Cholesky_] triat provides methods for Cholesky decomposition for symmetric/hermite positive dinite matrix.
//!
//! Eigenvalue Problem
//! -------------------
//!
//! According to the property input metrix,
//! there are several types of eigenvalue problem API
//!
//! - [Eig_] trait provides methods for eigenvalue problem for general matrix.
//! - [Eigh_] trait provides methods for eigenvalue problem for symmetric/hermite matrix.
//!
//! Singular Value Decomposition (SVD), Least square problem
//! ----------------------------------------------------------
//!
//! | matrix type  | Singular Value Decomposition (SVD) | SVD with divided-and-conquer (SDD) | Least square problem (LSD) |
//! |:-------------|:-----------------------------------|:-----------------------------------|:---------------------------|
//! | General (GE) | [svd]                              | [svddc]                            | [least_squares]            |
//!
//! [svd]:   svd/trait.SVD_.html#tymethod.svd
//! [svddc]: svddck/trait.SVDDC_.html#tymethod.svddc
//! [least_squares]: least_squares/trait.LeastSquaresSvdDivideConquer_.html#tymethod.least_squares

#[cfg(any(feature = "intel-mkl-system", feature = "intel-mkl-static"))]
extern crate intel_mkl_src as _src;

#[cfg(any(feature = "openblas-system", feature = "openblas-static"))]
extern crate openblas_src as _src;

#[cfg(any(feature = "netlib-system", feature = "netlib-static"))]
extern crate netlib_src as _src;

pub mod error;
pub mod flags;
pub mod layout;

mod cholesky;
mod eig;
mod eigh;
mod least_squares;
mod opnorm;
mod qr;
mod rcond;
mod solve;
mod solveh;
mod svd;
mod svddc;
mod triangular;
mod tridiagonal;

pub use self::cholesky::*;
pub use self::eig::*;
pub use self::eigh::*;
pub use self::flags::*;
pub use self::least_squares::*;
pub use self::opnorm::*;
pub use self::qr::*;
pub use self::rcond::*;
pub use self::solve::*;
pub use self::solveh::*;
pub use self::svd::*;
pub use self::svddc::*;
pub use self::triangular::*;
pub use self::tridiagonal::*;

use cauchy::*;
use std::mem::MaybeUninit;

pub type Pivot = Vec<i32>;

/// Trait for primitive types which implements LAPACK subroutines
pub trait Lapack:
    OperatorNorm_
    + QR_
    + SVD_
    + SVDDC_
    + Solve_
    + Solveh_
    + Cholesky_
    + Eig_
    + Eigh_
    + Triangular_
    + Tridiagonal_
    + Rcond_
    + LeastSquaresSvdDivideConquer_
{
}

impl Lapack for f32 {}
impl Lapack for f64 {}
impl Lapack for c32 {}
impl Lapack for c64 {}

/// Helper for getting pointer of slice
pub(crate) trait AsPtr: Sized {
    type Elem;
    fn as_ptr(vec: &[Self]) -> *const Self::Elem;
    fn as_mut_ptr(vec: &mut [Self]) -> *mut Self::Elem;
}

macro_rules! impl_as_ptr {
    ($target:ty, $elem:ty) => {
        impl AsPtr for $target {
            type Elem = $elem;
            fn as_ptr(vec: &[Self]) -> *const Self::Elem {
                vec.as_ptr() as *const _
            }
            fn as_mut_ptr(vec: &mut [Self]) -> *mut Self::Elem {
                vec.as_mut_ptr() as *mut _
            }
        }
    };
}
impl_as_ptr!(i32, i32);
impl_as_ptr!(f32, f32);
impl_as_ptr!(f64, f64);
impl_as_ptr!(c32, lapack_sys::__BindgenComplex<f32>);
impl_as_ptr!(c64, lapack_sys::__BindgenComplex<f64>);
impl_as_ptr!(MaybeUninit<i32>, i32);
impl_as_ptr!(MaybeUninit<f32>, f32);
impl_as_ptr!(MaybeUninit<f64>, f64);
impl_as_ptr!(MaybeUninit<c32>, lapack_sys::__BindgenComplex<f32>);
impl_as_ptr!(MaybeUninit<c64>, lapack_sys::__BindgenComplex<f64>);

pub(crate) trait VecAssumeInit {
    type Target;
    unsafe fn assume_init(self) -> Self::Target;
}

impl<T> VecAssumeInit for Vec<MaybeUninit<T>> {
    type Target = Vec<T>;
    unsafe fn assume_init(self) -> Self::Target {
        // FIXME use Vec::into_raw_parts instead after stablized
        // https://doc.rust-lang.org/std/vec/struct.Vec.html#method.into_raw_parts
        let mut me = std::mem::ManuallyDrop::new(self);
        Vec::from_raw_parts(me.as_mut_ptr() as *mut T, me.len(), me.capacity())
    }
}

/// Create a vector without initialization
///
/// Safety
/// ------
/// - Memory is not initialized. Do not read the memory before write.
///
unsafe fn vec_uninit<T: Sized>(n: usize) -> Vec<MaybeUninit<T>> {
    let mut v = Vec::with_capacity(n);
    v.set_len(n);
    v
}