1
  2
  3
  4
  5
  6
  7
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
//! Solve Hermitian (or real symmetric) linear problems and invert Hermitian
//! (or real symmetric) matrices
//!
//! **Note that only the upper triangular portion of the matrix is used.**
//!
//! # Examples
//!
//! Solve `A * x = b`, where `A` is a Hermitian (or real symmetric) matrix:
//!
//! ```
//! #[macro_use]
//! extern crate ndarray;
//! extern crate ndarray_linalg;
//!
//! use ndarray::prelude::*;
//! use ndarray_linalg::SolveH;
//! # fn main() {
//!
//! let a: Array2<f64> = array![
//!     [3., 2., -1.],
//!     [2., -2., 4.],
//!     [-1., 4., 5.]
//! ];
//! let b: Array1<f64> = array![11., -12., 1.];
//! let x = a.solveh_into(b).unwrap();
//! assert!(x.all_close(&array![1., 3., -2.], 1e-9));
//!
//! # }
//! ```
//!
//! If you are solving multiple systems of linear equations with the same
//! Hermitian or real symmetric coefficient matrix `A`, it's faster to compute
//! the factorization once at the beginning than solving directly using `A`:
//!
//! ```
//! # extern crate ndarray;
//! # extern crate ndarray_linalg;
//! use ndarray::prelude::*;
//! use ndarray_linalg::*;
//! # fn main() {
//!
//! let a: Array2<f64> = random((3, 3));
//! let f = a.factorizeh_into().unwrap(); // Factorize A (A is consumed)
//! for _ in 0..10 {
//!     let b: Array1<f64> = random(3);
//!     let x = f.solveh_into(b).unwrap(); // Solve A * x = b using the factorization
//! }
//!
//! # }
//! ```

use ndarray::*;

use super::convert::*;
use super::error::*;
use super::layout::*;
use super::types::*;

pub use lapack_traits::{Pivot, UPLO};

/// An interface for solving systems of Hermitian (or real symmetric) linear equations.
///
/// If you plan to solve many equations with the same Hermitian (or real
/// symmetric) coefficient matrix `A` but different `b` vectors, it's faster to
/// factor the `A` matrix once using the `FactorizeH` trait, and then solve
/// using the `FactorizedH` struct.
pub trait SolveH<A: Scalar> {
    /// Solves a system of linear equations `A * x = b` with Hermitian (or real
    /// symmetric) matrix `A`, where `A` is `self`, `b` is the argument, and
    /// `x` is the successful result.
    fn solveh<S: Data<Elem = A>>(&self, b: &ArrayBase<S, Ix1>) -> Result<Array1<A>> {
        let mut b = replicate(b);
        self.solveh_mut(&mut b)?;
        Ok(b)
    }
    /// Solves a system of linear equations `A * x = b` with Hermitian (or real
    /// symmetric) matrix `A`, where `A` is `self`, `b` is the argument, and
    /// `x` is the successful result.
    fn solveh_into<S: DataMut<Elem = A>>(&self, mut b: ArrayBase<S, Ix1>) -> Result<ArrayBase<S, Ix1>> {
        self.solveh_mut(&mut b)?;
        Ok(b)
    }
    /// Solves a system of linear equations `A * x = b` with Hermitian (or real
    /// symmetric) matrix `A`, where `A` is `self`, `b` is the argument, and
    /// `x` is the successful result. The value of `x` is also assigned to the
    /// argument.
    fn solveh_mut<'a, S: DataMut<Elem = A>>(&self, &'a mut ArrayBase<S, Ix1>) -> Result<&'a mut ArrayBase<S, Ix1>>;
}

/// Represents the Bunch–Kaufman factorization of a Hermitian (or real
/// symmetric) matrix as `A = P * U * D * U^H * P^T`.
pub struct FactorizedH<S: Data> {
    pub a: ArrayBase<S, Ix2>,
    pub ipiv: Pivot,
}

impl<A, S> SolveH<A> for FactorizedH<S>
where
    A: Scalar,
    S: Data<Elem = A>,
{
    fn solveh_mut<'a, Sb>(&self, rhs: &'a mut ArrayBase<Sb, Ix1>) -> Result<&'a mut ArrayBase<Sb, Ix1>>
    where
        Sb: DataMut<Elem = A>,
    {
        unsafe {
            A::solveh(
                self.a.square_layout()?,
                UPLO::Upper,
                self.a.as_allocated()?,
                &self.ipiv,
                rhs.as_slice_mut().unwrap(),
            )?
        };
        Ok(rhs)
    }
}

impl<A, S> SolveH<A> for ArrayBase<S, Ix2>
where
    A: Scalar,
    S: Data<Elem = A>,
{
    fn solveh_mut<'a, Sb>(&self, mut rhs: &'a mut ArrayBase<Sb, Ix1>) -> Result<&'a mut ArrayBase<Sb, Ix1>>
    where
        Sb: DataMut<Elem = A>,
    {
        let f = self.factorizeh()?;
        f.solveh_mut(rhs)
    }
}


impl<A, S> FactorizedH<S>
where
    A: Scalar,
    S: DataMut<Elem = A>,
{
    /// Computes the inverse of the factorized matrix.
    ///
    /// **Warning: The inverse is stored only in the upper triangular portion
    /// of the result matrix!** If you want the lower triangular portion to be
    /// correct, you must fill it in according to the results in the upper
    /// triangular portion.
    pub fn into_inverseh(mut self) -> Result<ArrayBase<S, Ix2>> {
        unsafe {
            A::invh(
                self.a.square_layout()?,
                UPLO::Upper,
                self.a.as_allocated_mut()?,
                &self.ipiv,
            )?
        };
        Ok(self.a)
    }
}

/// An interface for computing the Bunch–Kaufman factorization of Hermitian (or
/// real symmetric) matrix refs.
pub trait FactorizeH<S: Data> {
    /// Computes the Bunch–Kaufman factorization of a Hermitian (or real
    /// symmetric) matrix.
    fn factorizeh(&self) -> Result<FactorizedH<S>>;
}

/// An interface for computing the Bunch–Kaufman factorization of Hermitian (or
/// real symmetric) matrices.
pub trait FactorizeHInto<S: Data> {
    /// Computes the Bunch–Kaufman factorization of a Hermitian (or real
    /// symmetric) matrix.
    fn factorizeh_into(self) -> Result<FactorizedH<S>>;
}

impl<A, S> FactorizeHInto<S> for ArrayBase<S, Ix2>
where
    A: Scalar,
    S: DataMut<Elem = A>,
{
    fn factorizeh_into(mut self) -> Result<FactorizedH<S>> {
        let ipiv = unsafe { A::bk(self.layout()?, UPLO::Upper, self.as_allocated_mut()?)? };
        Ok(FactorizedH {
            a: self,
            ipiv: ipiv,
        })
    }
}

impl<A, Si> FactorizeH<OwnedRepr<A>> for ArrayBase<Si, Ix2>
where
    A: Scalar,
    Si: Data<Elem = A>,
{
    fn factorizeh(&self) -> Result<FactorizedH<OwnedRepr<A>>> {
        let mut a: Array2<A> = replicate(self);
        let ipiv = unsafe { A::bk(a.layout()?, UPLO::Upper, a.as_allocated_mut()?)? };
        Ok(FactorizedH { a: a, ipiv: ipiv })
    }
}

/// An interface for inverting Hermitian (or real symmetric) matrix refs.
pub trait InverseH {
    type Output;
    /// Computes the inverse of the Hermitian (or real symmetric) matrix.
    ///
    /// **Warning: The inverse is stored only in the upper triangular portion
    /// of the result matrix!** If you want the lower triangular portion to be
    /// correct, you must fill it in according to the results in the upper
    /// triangular portion.
    fn invh(&self) -> Result<Self::Output>;
}

/// An interface for inverting Hermitian (or real symmetric) matrices.
pub trait InverseHInto {
    type Output;
    /// Computes the inverse of the Hermitian (or real symmetric) matrix.
    ///
    /// **Warning: The inverse is stored only in the upper triangular portion
    /// of the result matrix!** If you want the lower triangular portion to be
    /// correct, you must fill it in according to the results in the upper
    /// triangular portion.
    fn invh_into(self) -> Result<Self::Output>;
}

impl<A, S> InverseHInto for ArrayBase<S, Ix2>
where
    A: Scalar,
    S: DataMut<Elem = A>,
{
    type Output = Self;

    fn invh_into(self) -> Result<Self::Output> {
        let f = self.factorizeh_into()?;
        f.into_inverseh()
    }
}

impl<A, Si> InverseH for ArrayBase<Si, Ix2>
where
    A: Scalar,
    Si: Data<Elem = A>,
{
    type Output = Array2<A>;

    fn invh(&self) -> Result<Self::Output> {
        let f = self.factorizeh()?;
        f.into_inverseh()
    }
}