LinearSolve.jl: High-Performance Unified Interface for Linear Solvers in Julia. Easily switch between factorization and Krylov methods, add preconditioners, and all in one interface.
This allows to set default_alias_A=true for sparse factorizations (which just cannot overwrite A anyway). This allows to create the cache parametrized with the original matrix type and we can use reinit!(cache;A) or cache.A=A if A is an AbstractSparseMatrixCSC, like with Krylov solvers.
Both are subtypes of AbstractFactorization.
This allows to set default_alias_A=true for sparse factorizations (which just cannot overwrite A anyway). This allows to create the cache parametrized with the original matrix type and we can use
reinit!(cache;A)orcache.A=AifAis an AbstractSparseMatrixCSC, like with Krylov solvers.Before, due to https://github.com/SciML/LinearSolve.jl/pull/525, these methods would require a SparseMatrixCSC, even if the Problem was created with a AbstractSparseMatrixCSC.
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