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Update docs/src/tutorials/linear.md
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docs/src/tutorials/linear.md

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Similarly structure matrix types, like banded matrices, can be provided using special matrix
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types. While any `AbstractMatrix` type should be compatible via the general Julia interfaces,
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LinearSolve.jl specifically tests with the following cases:
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* [LinearAlgebra.jl](https://docs.julialang.org/en/v1/stdlib/LinearAlgebra/)
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* Symmetric
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* Hermitian
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* UpperTriangular
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* UnitUpperTriangular
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* LowerTriangular
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* UnitLowerTriangular
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* SymTridiagonal
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* Tridiagonal
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* Bidiagonal
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* Diagonal
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* [BandedMatrices.jl](https://github.com/JuliaLinearAlgebra/BandedMatrices.jl) `BandedMatrix`
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* [BlockDiagonals.jl](https://github.com/JuliaArrays/BlockDiagonals.jl) `BlockDiagonal`
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* [CUDA.jl](https://cuda.juliagpu.org/stable/) (CUDA GPU-based dense and sparse matrices) `CuArray` (`GPUArray`)
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* [FastAlmostBandedMatrices.jl](https://github.com/SciML/FastAlmostBandedMatrices.jl) `FastAlmostBandedMatrix`
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* [Metal.jl](https://metal.juliagpu.org/stable/) (Apple M-series GPU-based dense matrices) `MetalArray`
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LinearSolve.jl specifically tests with the following cases:
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- [LinearAlgebra.jl](https://docs.julialang.org/en/v1/stdlib/LinearAlgebra/)
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+ Symmetric
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+ Hermitian
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+ UpperTriangular
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+ UnitUpperTriangular
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+ LowerTriangular
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+ UnitLowerTriangular
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+ SymTridiagonal
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+ Tridiagonal
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+ Bidiagonal
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+ Diagonal
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- [BandedMatrices.jl](https://github.com/JuliaLinearAlgebra/BandedMatrices.jl) `BandedMatrix`
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- [BlockDiagonals.jl](https://github.com/JuliaArrays/BlockDiagonals.jl) `BlockDiagonal`
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- [CUDA.jl](https://cuda.juliagpu.org/stable/) (CUDA GPU-based dense and sparse matrices) `CuArray` (`GPUArray`)
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- [FastAlmostBandedMatrices.jl](https://github.com/SciML/FastAlmostBandedMatrices.jl) `FastAlmostBandedMatrix`
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- [Metal.jl](https://metal.juliagpu.org/stable/) (Apple M-series GPU-based dense matrices) `MetalArray`
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!!! note
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