Release Notes

v4.2

New GESVFactorization algorithm mirroring LAPACK's gesv driver: fresh matrices factorize and solve in a single LAPACK.gesv! call, and repeat solves with only a new b reuse the cached factors through an allocation-free LAPACK.getrs!.
Dense LUFactorization refactorizations (cache.A = X then solve!) now reuse the cached pivot vector (and, without alias_A, the cached factors buffer) on Julia >= 1.11, making warm refactorization solves allocation-free.
The dense LUFactorization pivot-buffer reuse with alias_A = true now also covers the generic-kernel path (NoPivot/RowNonZero pivoting and non-BLAS element types), on all supported Julia versions.

Batched (matrix) right-hand sides are now supported: solve(LinearProblem(A, B)) with B::AbstractMatrix computes the equivalent of A \ B, factorizing A once and returning sol.u as a size(A, 2) × size(B, 2) matrix. This is a breaking change: previously a matrix b initialized a vector-shaped u and generally errored downstream. Batched right-hand sides are supported by the factorization-based algorithms; iterative (Krylov) methods throw an informative ArgumentError for matrix b.

CudaOffloadFactorization has been split into two algorithms:
- CudaOffloadLUFactorization - Uses LU factorization for better performance
- CudaOffloadQRFactorization - Uses QR factorization for better numerical stability
CudaOffloadFactorization is now deprecated and will show a warning suggesting to use one of the new algorithms

LinearCache changed from immutable to mutable. With this, the out of place interfaces like set_A were deprecated for simply mutating the cache, cache.A = .... This fixes some correctness checks and makes the package more robust while improving performance.
The default algorithm is now type-stable and does not rely on a dynamic dispatch for the choice.
IterativeSolvers.jl and KrylovKit.jl were made into extension packages.
Documentation of the solvers has changed to docstrings