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Fix outer comments
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‎Cargo.toml‎

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@@ -13,6 +13,7 @@ members = [
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"sprs-benches",
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"sprs-tests",
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]
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resolver = "2"
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[workspace.package]
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rust-version = "1.64"

‎sprs-ldl/src/lib.rs‎

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///! Cholesky factorization module.
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///!
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///! Contains LDLT decomposition methods.
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///!
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///! This decomposition operates on symmetric positive definite matrices,
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///! and is written `A = L D L` where L is lower triangular and D is diagonal.
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///! It is closely related to the Cholesky decomposition, but is often more
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///! numerically stable and can work on some indefinite matrices.
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///!
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///! The easiest way to use this API is to create a `LdlNumeric` instance from
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///! a matrix, then use the `LdlNumeric::solve` method.
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///!
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///! It is possible to update a decomposition if the sparsity structure of a
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///! matrix does not change. In that case the `LdlNumeric::update` method can
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///! be used.
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///!
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///! When only the sparsity structure of a matrix is known, it is possible
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///! to precompute part of the factorization by using the `LdlSymbolic` struct.
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///! This struct can the be converted into a `LdlNumeric` once the non-zero
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///! values are known, using the `LdlSymbolic::factor` method.
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// This method is adapted from the LDL library by Tim Davis:
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//
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// LDL Copyright (c) 2005 by Timothy A. Davis. All Rights Reserved.
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//
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// LDL License:
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//
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// Your use or distribution of LDL or any modified version of
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// LDL implies that you agree to this License.
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//
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// This library is free software; you can redistribute it and/or
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// modify it under the terms of the GNU Lesser General Public
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// License as published by the Free Software Foundation; either
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// version 2.1 of the License, or (at your option) any later version.
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//
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// This library is distributed in the hope that it will be useful,
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// but WITHOUT ANY WARRANTY; without even the implied warranty of
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// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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// Lesser General Public License for more details.
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//
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// You should have received a copy of the GNU Lesser General Public
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// License along with this library; if not, write to the Free Software
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// Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301
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// USA
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//
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// Permission is hereby granted to use or copy this program under the
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// terms of the GNU LGPL, provided that the Copyright, this License,
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// and the Availability of the original version is retained on all copies.
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// User documentation of any code that uses this code or any modified
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// version of this code must cite the Copyright, this License, the
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// Availability note, and "Used by permission." Permission to modify
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// the code and to distribute modified code is granted, provided the
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// Copyright, this License, and the Availability note are retained,
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// and a notice that the code was modified is included.
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//! Cholesky factorization module.
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//!
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//! Contains LDLT decomposition methods.
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//!
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//! This decomposition operates on symmetric positive definite matrices,
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//! and is written `A = L D L` where L is lower triangular and D is diagonal.
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//! It is closely related to the Cholesky decomposition, but is often more
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//! numerically stable and can work on some indefinite matrices.
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//!
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//! The easiest way to use this API is to create a `LdlNumeric` instance from
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//! a matrix, then use the `LdlNumeric::solve` method.
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//!
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//! It is possible to update a decomposition if the sparsity structure of a
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//! matrix does not change. In that case the `LdlNumeric::update` method can
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//! be used.
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//!
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//! When only the sparsity structure of a matrix is known, it is possible
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//! to precompute part of the factorization by using the `LdlSymbolic` struct.
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//! This struct can the be converted into a `LdlNumeric` once the non-zero
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//! values are known, using the `LdlSymbolic::factor` method.
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//!
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//! This method is adapted from the LDL library by Tim Davis:
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//!
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//! LDL Copyright (c) 2005 by Timothy A. Davis. All Rights Reserved.
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//!
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//! LDL License:
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//!
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//! Your use or distribution of LDL or any modified version of
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//! LDL implies that you agree to this License.
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//!
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//! This library is free software; you can redistribute it and/or
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//! modify it under the terms of the GNU Lesser General Public
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//! License as published by the Free Software Foundation; either
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//! version 2.1 of the License, or (at your option) any later version.
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//!
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//! This library is distributed in the hope that it will be useful,
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//! but WITHOUT ANY WARRANTY; without even the implied warranty of
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//! MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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//! Lesser General Public License for more details.
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//!
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//! You should have received a copy of the GNU Lesser General Public
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//! License along with this library; if not, write to the Free Software
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//! Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301
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//! USA
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//!
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//! Permission is hereby granted to use or copy this program under the
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//! terms of the GNU LGPL, provided that the Copyright, this License,
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//! and the Availability of the original version is retained on all copies.
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//! User documentation of any code that uses this code or any modified
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//! version of this code must cite the Copyright, this License, the
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//! Availability note, and "Used by permission." Permission to modify
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//! the code and to distribute modified code is granted, provided the
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//! Copyright, this License, and the Availability note are retained,
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//! and a notice that the code was modified is included.
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use std::ops::Deref;
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use num_traits::Num;

‎sprs/src/indexing.rs‎

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//! Abstraction over types of indices
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//!
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//! Our sparse matrices can use any integer type for its indices among
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//! `u16, u32, u64, usize, i16, i32, i64, isize`.
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//!
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//! By default, sprs matrices will use `usize`, but it can be useful to switch
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//! to another index type to reduce the memory usage of sparse matrices, of for
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//! compatibility purposes when calling into an existing library through FFI.
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use std::fmt::Debug;
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///! Abstraction over types of indices
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///!
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///! Our sparse matrices can use any integer type for its indices among
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///! `u16, u32, u64, usize, i16, i32, i64, isize`.
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///!
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///! By default, sprs matrices will use `usize`, but it can be useful to switch
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///! to another index type to reduce the memory usage of sparse matrices, of for
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///! compatibility purposes when calling into an existing library through FFI.
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use std::ops::AddAssign;
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use num_traits::int::PrimInt;

‎sprs/src/sparse/compressed.rs‎

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//! Traits to generalize over compressed sparse matrices storages
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use crate::indexing::SpIndex;
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///! Traits to generalize over compressed sparse matrices storages
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use crate::sparse::prelude::*;
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use std::ops::Deref;
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‎sprs/src/sparse/csmat.rs‎

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//! A sparse matrix in the Compressed Sparse Row/Column format
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//!
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//! In the CSR format, a matrix is a structure containing three vectors:
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//! indptr, indices, and data
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//! These vectors satisfy the relation
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//! for i in [0, nrows],
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//! A(i, indices[indptr[i]..indptr[i+1]]) = data[indptr[i]..indptr[i+1]]
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//! In the CSC format, the relation is
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//! A(indices[indptr[i]..indptr[i+1]], i) = data[indptr[i]..indptr[i+1]]
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use ndarray::ArrayView;
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use num_traits::{Float, Num, Signed, Zero};
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#[cfg(feature = "serde")]
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use serde::{Deserialize, Serialize};
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use std::cmp;
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///! A sparse matrix in the Compressed Sparse Row/Column format
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///
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/// In the CSR format, a matrix is a structure containing three vectors:
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/// indptr, indices, and data
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/// These vectors satisfy the relation
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/// for i in [0, nrows],
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/// A(i, indices[indptr[i]..indptr[i+1]]) = data[indptr[i]..indptr[i+1]]
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/// In the CSC format, the relation is
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/// A(indices[indptr[i]..indptr[i+1]], i) = data[indptr[i]..indptr[i+1]]
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use std::default::Default;
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use std::iter::{Enumerate, Zip};
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use std::mem;

‎sprs/src/sparse/linalg.rs‎

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//! Sparse linear algebra
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//!
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//! This module contains solvers for sparse linear systems. Currently
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//! there are solver for sparse triangular systems and symmetric systems.
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use crate::{DenseVector, DenseVectorMut};
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///! Sparse linear algebra
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///!
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///! This module contains solvers for sparse linear systems. Currently
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///! there are solver for sparse triangular systems and symmetric systems.
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use num_traits::Num;
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pub mod etree;

‎sprs/src/sparse/linalg/etree.rs‎

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///! Data structures to work with elimination trees (etree).
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///! etrees arise when considering cholesky factorization, QR factorization, ...
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//! Data structures to work with elimination trees (etree).
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//! etrees arise when considering cholesky factorization, QR factorization, ...
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use std::ops::{Deref, DerefMut};
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pub type Parent = Option<usize>;

‎sprs/src/sparse/prod.rs‎

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//! Sparse matrix product
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use crate::dense_vector::{DenseVector, DenseVectorMut};
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use crate::indexing::SpIndex;
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use crate::sparse::compressed::SpMatView;
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///! Sparse matrix product
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use crate::sparse::prelude::*;
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use crate::Ix2;
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use ndarray::{ArrayView, ArrayViewMut, Axis};

‎sprs/src/sparse/special_mats.rs‎

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//! Common sparse matrices
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use ndarray::ArrayView2;
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///! Common sparse matrices
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use smallvec::SmallVec;
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use crate::indexing::SpIndex;

‎sprs/src/sparse/to_dense.rs‎

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//! Utilities for sparse-to-dense conversion
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use super::{CsMatViewI, CsVecViewI};
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use crate::indexing::SpIndex;
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use crate::{Ix1, Ix2};
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///! Utilities for sparse-to-dense conversion
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use ndarray::{ArrayViewMut, Axis};
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/// Assign a sparse matrix into a dense matrix

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