v2.39.1

LinearSolve v2.39.1

Diff since v2.39.0

This release has been identified as a backport.
Automated changelogs for backports tend to be wildly incorrect.
Therefore, the list of issues and pull requests is hidden.

v3.7.0

LinearSolve v3.7.0

Diff since v3.6.0

Merged pull requests:

• Removed deprecated functionality (#588) (@ChrisRackauckas)

v3.6.0

LinearSolve v3.6.0

Diff since v3.5.0

v3.5.0

LinearSolve v3.5.0

Diff since v3.4.0

Merged pull requests:

• Update adjoint of Linear Solve for complex matrices (#582) (@cmrace)
• CompatHelper: bump compat for BlockDiagonals in [weakdeps] to 0.2, (keep existing compat) (#585) (@github-actions[bot])
• Throw proper error for Sparspak missing (#586) (@ChrisRackauckas)
• Fix CUDA tests (#587) (@ChrisRackauckas)

v3.4.0

LinearSolve v3.4.0

Diff since v3.3.1

Merged pull requests:

• Make SparseArrays a weakdeps and add it as dependency of Sparspak and Pardiso extensions (#584) (@devmotion)

Closed issues:

• Malformed Project.toml (#583)

v3.3.1

LinearSolve v3.3.1

Diff since v3.2.0

Merged pull requests:

• Better handle Sparspak not loaded by default (#580) (@ChrisRackauckas)

v3.3.0

LinearSolve v3.3.0

Diff since v3.2.0

Merged pull requests:

• Better handle Sparspak not loaded by default (#580) (@ChrisRackauckas)

v3.2.0

LinearSolve v3.2.0

Diff since v3.1.0

Merged pull requests:

• Fix sparspak init_cacheval (#579) (@ErikQQY)

Closed issues:

• Extra memory usage that cannot be released from GC when reuse_symbolic=true (#233)
• LinearSolve missing methods for do_factorization on SparspakFactorization (#578)

v3.1.0

LinearSolve v3.1.0

Diff since v3.0.0

Merged pull requests:

• fix typo: makeempty_SparaseMatrixCSC -> makeempty_SparseMatrixCSC (#574) (@j-fu)
• Make RAT a hard dep (#577) (@ChrisRackauckas)

Closed issues:

• Circular dependency on 1.10 (#573)
• Julia 1.10 compatabilities (#576)

v3.0.0

LinearSolve v3.0.0

Diff since v2.39.0

Breaking changes

• RecrusiveFactorization.jl removed as a dependency and turned into an extension. It now must be explicitly loaded (using RecrusiveFactorization) in order to be used. It's still a part of the default algorithm but will only be selected if loaded. The reason for this is because this method brings in the LoopVectorization.jl stack and is thus a heavy dependency that can invalidate a large amount of code. This greatly reduces first run and solve times downstream. However, as it is the fastest method for many scenarios, it is still recommended that many users opt-in if they are looking for performance, but we see this makes a better trade-off between default performance and first run times.
• FastLapackInterface.jl removed as a dependency and turn into an extension. It's not actually faster so it was unused, so this is a simple dependency reduction.
• SparseArrays.jl removed as a dependency and turned into an extension. This allows for more easily building SciML packages in a GPL-free way (since SuiteSparse is GPL and pulled in through SparseArrays.jl), and also can greatly improve load times. However, in its current form it does not actually make a change because Krylov.jl, a hard dependency, still does using SparseArrays, which always triggers the extension. However, that should be solved soon (see JuliaSmoothOptimizers/Krylov.jl#955) in which case SparseArrays will no longer be required.

Merged pull requests:

• Use Aliasing API for alias_A and alias_b (#564) (@jClugstor)
• Make RecursiveFactorization.jl optional (#569) (@ChrisRackauckas)
• Make SparseArrays an extension (#570) (@ChrisRackauckas)
• Make FastLapackInterface.jl an extension as well (#572) (@ChrisRackauckas)