oxiblas-lapack 0.2.2

LAPACK operations for OxiBLAS - pure Rust implementation
Documentation 
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

//! OxiBLAS LAPACK - Pure Rust LAPACK implementation.
//!
//! This crate provides LAPACK (Linear Algebra PACKage) operations
//! implemented in pure Rust.
//!
//! # Decompositions
//!
//! - **LU**: LU decomposition with partial/full pivoting ✓
//! - **Cholesky**: LL^T and LDL^T decomposition for positive definite matrices ✓
//! - **QR**: QR decomposition with optional column pivoting ✓
//! - **EVD**: Eigenvalue decomposition (symmetric and general) ✓
//! - **SVD**: Singular value decomposition (Jacobi, divide-and-conquer, randomized) ✓
//!
//! # Example
//!
//! ```
//! use oxiblas_lapack::lu::Lu;
//! use oxiblas_matrix::Mat;
//!
//! let a: Mat<f64> = Mat::from_rows(&[
//!     &[2.0, 1.0],
//!     &[1.0, 3.0],
//! ]);
//!
//! let lu = Lu::compute(a.as_ref()).expect("Matrix is not singular");
//! let det = lu.determinant();
//! assert!((det - 5.0).abs() < 1e-10); // det = 2*3 - 1*1 = 5
//! ```

#![warn(missing_docs)]
#![warn(clippy::all)]
#![allow(clippy::module_name_repetitions)]
// LAPACK code often uses manual assign patterns for clarity
#![allow(clippy::assign_op_pattern)]
// Numerical code often has complex generic types
#![allow(clippy::type_complexity)]
// Loop index variables are common in matrix operations
#![allow(clippy::needless_range_loop)]
// LAPACK functions have many parameters by design
#![allow(clippy::too_many_arguments)]
// Bounds in two places for clarity in generic numerical code
#![allow(clippy::multiple_bound_locations)]
// Doc formatting variations
#![allow(clippy::doc_overindented_list_items)]
// Vec vs slice in internal APIs is acceptable
#![allow(clippy::ptr_arg)]
// Manual slice copying for explicit control
#![allow(clippy::manual_memcpy)]
// Balance algorithm has intentional range mutation
#![allow(clippy::mut_range_bound)]
// Iterative algorithms may have unusual patterns
#![allow(clippy::collapsible_if)]
#![allow(clippy::only_used_in_recursion)]
#![allow(clippy::manual_strip)]
#![allow(clippy::iter_cloned_collect)]
#![allow(clippy::nonminimal_bool)]
#![allow(clippy::eq_op)]
#![allow(clippy::unwrap_or_default)]
#![allow(clippy::unnecessary_unwrap)]
#![allow(clippy::neg_cmp_op_on_partial_ord)]

pub mod cholesky;
pub mod error;
pub mod evd;
pub mod info;
pub mod lu;
pub mod qr;
pub mod solve;
pub mod svd;
pub mod utils;
pub mod workspace;

/// Prelude module for convenient imports.
pub mod prelude {
    pub use crate::cholesky::{
        BandCholesky, BandCholeskyError, Cholesky, CholeskyError, Ldlt, LdltError,
        band_lower_to_dense, dense_to_band_lower,
    };
    pub use crate::error::{
        ErrorCategory, ErrorCode, HasInfoCode, INFO_SUCCESS, InfoCode, IntoLapackError,
        LapackError, LapackResult,
    };
    pub use crate::evd::{
        Eigenvalue, EigenvalueSelector, GeneralEvd, GeneralEvdError, Hessenberg, HessenbergError,
        Schur, SchurError, SymmetricEvd, SymmetricEvdDc, SymmetricEvdDcError, SymmetricEvdError,
        TridiagEvd, TridiagEvdError, count_eigenvalues, eigenvalue_bounds, eigenvalues_by_index,
        eigenvalues_in_range,
    };
    pub use crate::info::{
        CholeskyInfo, EvdInfo, LuInfo, QrInfo, SvdInfo, SymmetricEvdInfo, compute_cholesky_info,
        compute_general_evd_info, compute_lu_info, compute_qr_info, compute_svd_info,
        compute_symmetric_evd_info,
    };
    pub use crate::lu::{
        BandLu, BandLuError, Lu, LuError, LuFullPiv, LuFullPivError, band_to_dense, dense_to_band,
    };
    pub use crate::qr::{Qr, QrError, QrPivot, QrPivotError};
    pub use crate::solve::{
        Equilibrate, ExpertCholeskySolveError, ExpertCholeskySolveResult, ExpertSolveError,
        ExpertSolveResult, ExpertSymmetricSolveError, ExpertSymmetricSolveResult,
        LeastSquaresResult, LstSqError, RefinementError, RefinementResult, SolveError,
        TriangularKind, TriangularSolveError, TridiagError, TridiagFactors, TridiagSPDFactors,
        lstsq, refine_solution, refine_solution_cholesky, refine_solution_symmetric, solve,
        solve_cholesky_expert, solve_expert, solve_multiple, solve_symmetric_expert,
        solve_triangular, solve_triangular_multiple, tridiag_factor, tridiag_factor_spd,
        tridiag_solve, tridiag_solve_factored, tridiag_solve_factored_spd, tridiag_solve_multiple,
        tridiag_solve_spd,
    };
    pub use crate::svd::{
        BidiagError, BidiagFactors, BidiagVect, RandomizedSvd, RandomizedSvdConfig,
        RandomizedSvdError, SelectiveSvd, SelectiveSvdError, Side, SingularValueSelector, Svd,
        SvdDc, SvdDcError, SvdError, Trans, count_singular_values_above, gebrd,
        low_rank_approximation, orgbr, ormbr, rsvd, rsvd_power, singular_value_bounds, ungbr,
        unmbr,
    };
    pub use crate::utils::{
        CondError, DetError, InvError, PinvResult, RankError, col_space, cond, cond_1, cond_inf,
        det, det_lu, inv, left_null_space, norm_1, norm_2, norm_frobenius, norm_inf, norm_max,
        norm_nuclear, null_space, nullity, pinv, pinv_default, rank, rcond, rcond_estimate,
        row_space, trace,
    };
    pub use crate::workspace::{
        EvdWorkspaceQuery, SvdWorkspaceQuery, Workspace, WorkspaceQuery, WorkspaceQueryWithInt,
        band_lu_workspace, bidiag_workspace, cholesky_solve_workspace, cholesky_workspace,
        general_evd_workspace, generalized_evd_workspace, hermitian_evd_workspace,
        hessenberg_workspace, ldlt_workspace, least_squares_workspace, lu_solve_workspace,
        lu_workspace, optimal_block_size_bidiag, optimal_block_size_cholesky,
        optimal_block_size_evd, optimal_block_size_hessenberg, optimal_block_size_lu,
        optimal_block_size_qr, optimal_block_size_trsm, orgqr_workspace, ormqr_workspace,
        qr_pivot_workspace, qr_workspace, qz_workspace, schur_workspace, svd_dc_workspace,
        svd_workspace, symmetric_evd_dc_workspace, symmetric_evd_workspace,
        triangular_solve_workspace, tridiagonal_solve_workspace,
    };
}