From 6eb869c605e6147c8e2a2793afee619502463b1e Mon Sep 17 00:00:00 2001 From: ArnoStrouwen Date: Sun, 12 Feb 2023 23:20:28 +0100 Subject: [PATCH] strict links --- docs/make.jl | 2 +- docs/src/advanced/custom.md | 2 +- docs/src/basics/FAQ.md | 2 +- docs/src/basics/Preconditioners.md | 2 +- docs/src/solvers/solvers.md | 2 +- 5 files changed, 5 insertions(+), 5 deletions(-) diff --git a/docs/make.jl b/docs/make.jl index d449a3b25..dae4c0b62 100644 --- a/docs/make.jl +++ b/docs/make.jl @@ -11,7 +11,7 @@ include("pages.jl") makedocs(sitename = "LinearSolve.jl", authors = "Chris Rackauckas", modules = [LinearSolve, LinearSolve.SciMLBase], - clean = true, doctest = false, + clean = true, doctest = false, linkcheck = true, strict = [ :doctest, :linkcheck, diff --git a/docs/src/advanced/custom.md b/docs/src/advanced/custom.md index 927a7437a..ac86e1991 100644 --- a/docs/src/advanced/custom.md +++ b/docs/src/advanced/custom.md @@ -1,4 +1,4 @@ -# Passing in a Custom Linear Solver +# [Passing in a Custom Linear Solver](@id custom) Julia users are building a wide variety of applications in the SciML ecosystem, often requiring problem-specific handling of their linear solves. As existing solvers in `LinearSolve.jl` may not diff --git a/docs/src/basics/FAQ.md b/docs/src/basics/FAQ.md index b01c0af79..467d1a86d 100644 --- a/docs/src/basics/FAQ.md +++ b/docs/src/basics/FAQ.md @@ -11,7 +11,7 @@ efficiency and ability to choose solvers. ## Python's NumPy/SciPy just calls fast Fortran/C code, why would LinearSolve.jl be any better? -This is addressed in the [JuliaCon 2022 video](https://youtu.be/JWI34_w-yYw?t=182). This happens in +This is addressed in the [JuliaCon 2022 video](https://www.youtube.com/watch?v=JWI34_w-yYw&t=182s). This happens in a few ways: 1. The Fortran/C code that NumPy/SciPy uses is actually slow. It's [OpenBLAS](https://github.com/xianyi/OpenBLAS), diff --git a/docs/src/basics/Preconditioners.md b/docs/src/basics/Preconditioners.md index 37e8aead7..70b64a9d8 100644 --- a/docs/src/basics/Preconditioners.md +++ b/docs/src/basics/Preconditioners.md @@ -80,7 +80,7 @@ The following preconditioners match the interface of LinearSolve.jl. - [IncompleteLU.ilu](https://github.com/haampie/IncompleteLU.jl): an implementation of the incomplete LU-factorization preconditioner. This requires `A` as a `SparseMatrixCSC`. - - [Preconditioners.CholeskyPreconditioner(A, i)](https://github.com/mohamed82008/Preconditioners.jl): + - [Preconditioners.CholeskyPreconditioner(A, i)](https://github.com/JuliaLinearAlgebra/Preconditioners.jl): An incomplete Cholesky preconditioner with cut-off level `i`. Requires `A` as a `AbstractMatrix` and positive semi-definite. - [AlgebraicMultiGrid](https://github.com/JuliaLinearAlgebra/AlgebraicMultigrid.jl): diff --git a/docs/src/solvers/solvers.md b/docs/src/solvers/solvers.md index 6e858cfac..345a88f3f 100644 --- a/docs/src/solvers/solvers.md +++ b/docs/src/solvers/solvers.md @@ -35,7 +35,7 @@ with CPUs and GPUs, and thus is the generally preferred form for Krylov methods. Finally, a user can pass a custom function for handling the linear solve using `LinearSolveFunction()` if existing solvers are not optimally suited for their application. -The interface is detailed [here](#passing-in-a-custom-linear-solver). +The interface is detailed [here](@ref custom). ## Full List of Methods