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‎.github/dependabot.yml‎

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# https://docs.github.com/github/administering-a-repository/configuration-options-for-dependency-updates
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version: 2
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updates:
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- package-ecosystem: "github-actions"
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directory: "/" # Location of package manifests
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schedule:
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interval: "monthly"

‎.github/workflows/CompatHelper.yml‎

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name: CompatHelper
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on:
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schedule:
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- cron: 0 0 * * *
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workflow_dispatch:
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permissions:
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contents: write
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pull-requests: write
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jobs:
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CompatHelper:
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runs-on: ubuntu-latest
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steps:
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- name: Check if Julia is already available in the PATH
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id: julia_in_path
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run: which julia
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continue-on-error: true
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- name: Install Julia, but only if it is not already available in the PATH
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uses: julia-actions/setup-julia@v2
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with:
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version: '1'
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arch: ${{ runner.arch }}
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if: steps.julia_in_path.outcome != 'success'
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- name: "Add the General registry via Git"
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run: |
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import Pkg
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ENV["JULIA_PKG_SERVER"] = ""
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Pkg.Registry.add("General")
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shell: julia --color=yes {0}
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- name: "Install CompatHelper"
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run: |
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import Pkg
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name = "CompatHelper"
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uuid = "aa819f21-2bde-4658-8897-bab36330d9b7"
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version = "3"
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Pkg.add(; name, uuid, version)
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shell: julia --color=yes {0}
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- name: "Run CompatHelper"
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run: |
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import CompatHelper
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CompatHelper.main()
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shell: julia --color=yes {0}
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env:
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GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
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COMPATHELPER_PRIV: ${{ secrets.DOCUMENTER_KEY }}
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# COMPATHELPER_PRIV: ${{ secrets.COMPATHELPER_PRIV }}

‎.github/workflows/backport.yml‎

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if: github.event.pull_request.merged
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steps:
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- name: Backport
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uses: tibdex/backport@v1
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uses: tibdex/backport@v2
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with:
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github_token: ${{ secrets.GITHUB_TOKEN }}

‎.github/workflows/ci.yml‎

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name: CI
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on:
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pull_request:
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branches:
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- 'main'
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- 'release-*'
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push:
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branches:
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- 'main'
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cancel-in-progress: ${{ startsWith(github.ref, 'refs/pull/') }}
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jobs:
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test:
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name: Julia ${{ matrix.version }} - ${{ matrix.os }} - ${{ matrix.arch }} - ${{ github.event_name }}
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runs-on: ${{ matrix.os }}
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timeout-minutes: 60
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strategy:
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fail-fast: false
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matrix:
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version:
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julia-version:
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- 'nightly'
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os:
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- ubuntu-latest
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- macOS-latest
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- windows-latest
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arch:
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- macOS-13 # intel
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julia-arch:
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- x64
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- x86
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exclude:
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- os: macOS-13
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julia-arch: x86
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include:
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- os: macOS-latest
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arch: x86
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julia-arch: aarch64
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steps:
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- uses: actions/checkout@v2
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- uses: julia-actions/setup-julia@v1
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- uses: actions/checkout@v4
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- uses: julia-actions/setup-julia@v2
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with:
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version: ${{ matrix.version }}
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arch: ${{ matrix.arch }}
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- uses: actions/cache@v1
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version: ${{ matrix.julia-version }}
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arch: ${{ matrix.julia-arch }}
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- uses: actions/cache@v4
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env:
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cache-name: cache-artifacts
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with:
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- uses: julia-actions/julia-buildpkg@v1
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- uses: julia-actions/julia-runtest@v1
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- uses: julia-actions/julia-processcoverage@v1
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- uses: codecov/codecov-action@v1
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- uses: codecov/codecov-action@v4
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with:
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file: lcov.info
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token: ${{ secrets.CODECOV_TOKEN }}
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aqua-test:
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runs-on: ${{ matrix.os }}
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timeout-minutes: 60
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permissions: # needed to allow julia-actions/cache to proactively delete old caches that it has created
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actions: write
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contents: read
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strategy:
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matrix:
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julia-version:
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- 'nightly'
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os:
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- ubuntu-latest
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julia-arch:
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- x64
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steps:
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- uses: actions/checkout@v4
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- uses: julia-actions/setup-julia@v2
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with:
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version: ${{ matrix.julia-version }}
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arch: ${{ matrix.julia-arch }}
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- uses: julia-actions/cache@v2
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- run: julia --color=yes .ci/test_and_change_uuid.jl
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- uses: julia-actions/julia-buildpkg@v1
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- uses: julia-actions/julia-runtest@v1
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env:
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SPARSEARRAYS_AQUA_TEST: true
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docs:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v2
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- uses: julia-actions/setup-julia@latest
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- uses: actions/checkout@v4
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- uses: julia-actions/setup-julia@v2
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with:
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# version: '1.6'
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version: 'nightly'

‎.gitignore‎

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Manifest.toml
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Manifest-v*.*.toml
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docs/build

‎Project.toml‎

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name = "SparseArrays"
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uuid = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
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version = "1.10.0"
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version = "1.12.0"
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[deps]
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Libdl = "8f399da3-3557-5675-b5ff-fb832c97cbdb"
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SuiteSparse_jll = "bea87d4a-7f5b-5778-9afe-8cc45184846c"
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[compat]
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SuiteSparse_jll = "7.2"
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julia = "1.10"
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Aqua = "0.8"
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Dates = "<0.0.1, 1"
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InteractiveUtils = "<0.0.1, 1"
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Libdl = "<0.0.1, 1"
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LinearAlgebra = "<0.0.1, 1"
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Pkg = "<0.0.1, 1"
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Printf = "<0.0.1, 1"
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Random = "<0.0.1, 1"
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Serialization = "<0.0.1, 1"
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SuiteSparse_jll = "7.8.0"
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Test = "<0.0.1, 1"
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julia = "1.11"
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[extras]
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Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"

‎docs/Project.toml‎

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[deps]
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Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
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[compat]
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Documenter = "1"

‎docs/make.jl‎

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sitename = "SparseArrays",
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pages = Any[
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"SparseArrays" => "index.md",
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"Sparse Linear Algebra" => "solvers.md",
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"Sparse Linear Algebra API" => "solvers.md",
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];
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# strict = true,
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strict = Symbol[:doctest],
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warnonly = [:missing_docs, :cross_references],
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)
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deploydocs(repo = "github.com/JuliaSparse/SparseArrays.jl.git")

‎docs/src/index.md‎

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```@meta
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EditURL = "https://github.com/JuliaSparse/SparseArrays.jl/blob/master/docs/src/index.md"
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```
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# Sparse Arrays
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```@meta
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Julia has support for sparse vectors and [sparse matrices](https://en.wikipedia.org/wiki/Sparse_matrix)
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in the `SparseArrays` stdlib module. Sparse arrays are arrays that contain enough zeros that storing them in a special data structure leads to savings in space and execution time, compared to dense arrays.
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External packages which implement different sparse storage types, multidimensional sparse arrays, and more can be found in [Noteworthy external packages](@ref man-csc)
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External packages which implement different sparse storage types, multidimensional sparse arrays, and more can be found in [Noteworthy External Sparse Packages](@ref)
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## [Compressed Sparse Column (CSC) Sparse Matrix Storage](@id man-csc)
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```
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The inverse of the [`sparse`](@ref) and [`sparsevec`](@ref) functions is
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[`findnz`](@ref), which retrieves the inputs used to create the sparse array.
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[`findall(!iszero, x)`](@ref) returns the cartesian indices of non-zero entries in `x`
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(including stored entries equal to zero).
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[`findnz`](@ref), which retrieves the inputs used to create the sparse array (including stored entries equal to zero).
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[`findall(!iszero, x)`](@ref) returns the Cartesian indices of non-zero entries in `x`
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(not including stored entries equal to zero).
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```jldoctest sparse_function
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julia> findnz(S)
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| [`sprandn(m,n,d)`](@ref) | [`randn(m,n)`](@ref) | Creates a *m*-by-*n* random matrix (of density *d*) with iid non-zero elements distributed according to the standard normal (Gaussian) distribution. |
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| [`sprandn(rng,m,n,d)`](@ref) | [`randn(rng,m,n)`](@ref) | Creates a *m*-by-*n* random matrix (of density *d*) with iid non-zero elements generated with the `rng` random number generator |
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## [Sparse Linear Algebra](@id stdlib-sparse-linalg)
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Sparse matrix solvers call functions from [SuiteSparse](http://suitesparse.com). The following factorizations are available:
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1. [`cholesky`](@ref SparseArrays.CHOLMOD.cholesky)
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2. [`ldlt`](@ref SparseArrays.CHOLMOD.ldlt)
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3. [`lu`](@ref SparseArrays.UMFPACK.lu)
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4. [`qr`](@ref SparseArrays.SPQR.qr)
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| Type | Description |
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|:----------------------|:--------------------------------------------- |
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| `CHOLMOD.Factor` | Cholesky and LDLt factorizations |
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| `UMFPACK.UmfpackLU` | LU factorization |
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| `SPQR.QRSparse` | QR factorization |
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```@meta
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DocTestSetup = nothing
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```
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# [SparseArrays API](@id stdlib-sparse-arrays)
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```@docs
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```@meta
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DocTestSetup = nothing
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```
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# Noteworthy external packages
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# Noteworthy External Sparse Packages
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Several other Julia packages provide sparse matrix implementations that should be mentioned:
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6. [LuxurySparse.jl](https://github.com/QuantumBFS/LuxurySparse.jl) provides static sparse array formats, as well as a coordinate format.
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7. [ExtendableSparse.jl](https://github.com/j-fu/ExtendableSparse.jl) enables fast insertion into sparse matrices using a lazy approach to new stored indices.
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8. [Finch.jl](https://github.com/willow-ahrens/Finch.jl) supports extensive multidimensional sparse array formats and operations through a mini tensor language and compiler, all in native Julia. Support for COO, CSF, CSR, CSC and more, as well as operations like broadcast, reduce, etc. and custom operations.
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External packages providing sparse direct solvers:
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1. [KLU.jl](https://github.com/JuliaSparse/KLU.jl)
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2. [Pardiso.jl](https://github.com/JuliaSparse/Pardiso.jl/)
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External packages providing solvers for iterative solution of eigensystems and singular value decompositions:
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1. [ArnoldiMethods.jl](https://github.com/JuliaLinearAlgebra/ArnoldiMethod.jl)
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2. [KrylovKit](https://github.com/Jutho/KrylovKit.jl)
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3. [Arpack.jl](https://github.com/JuliaLinearAlgebra/Arpack.jl)
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External packages for working with graphs:
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1. [Graphs.jl](https://github.com/JuliaGraphs/Graphs.jl)

‎docs/src/solvers.md‎

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# Sparse Linear Algebra
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# Sparse Linear Algebra (API)
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```@meta
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DocTestSetup = :(using LinearAlgebra, SparseArrays)
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```
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Sparse matrix solvers call functions from [SuiteSparse](http://suitesparse.com). The following factorizations are available:
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| Type | Description |
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|:--------------------------------- |:--------------------------------------------- |
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| `CHOLMOD.Factor` | Cholesky factorization |
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| `UMFPACK.UmfpackLU` | LU factorization |
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| `SPQR.QRSparse` | QR factorization |
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Other solvers such as [Pardiso.jl](https://github.com/JuliaSparse/Pardiso.jl/) are available as external packages. [Arpack.jl](https://julialinearalgebra.github.io/Arpack.jl/stable/) provides `eigs` and `svds` for iterative solution of eigensystems and singular value decompositions.
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These factorizations are described in more detail in [`Linear Algebra`](https://docs.julialang.org/en/v1/stdlib/LinearAlgebra/) section of the manual:
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1. [`cholesky`](@ref)
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2. [`ldlt`](@ref)
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3. [`lu`](@ref)
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4. [`qr`](@ref)
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```@docs
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```@docs; canonical=false
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SparseArrays.CHOLMOD.cholesky
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SparseArrays.CHOLMOD.cholesky!
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SparseArrays.CHOLMOD.ldlt
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SparseArrays.SPQR.qr
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SparseArrays.UMFPACK.lu
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```
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```@docs
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SparseArrays.CHOLMOD.lowrankupdate
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SparseArrays.CHOLMOD.lowrankupdate!
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SparseArrays.CHOLMOD.lowrankdowndate
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SparseArrays.CHOLMOD.lowrankdowndate!
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SparseArrays.CHOLMOD.lowrankupdowndate!
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SparseArrays.CHOLMOD.ldlt
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SparseArrays.SPQR.qr
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SparseArrays.UMFPACK.lu
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```
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```@meta

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