Update factorization.jl

Author: ChrisRackauckas

src/factorization.jl

@@ -1111,61 +1111,6 @@ function SciMLBase.solve!(cache::LinearCache, alg::DiagonalFactorization;
     SciMLBase.build_linear_solution(alg, cache.u, nothing, cache)
 end
 
-## CUSOLVERRFFactorization
-
-"""
-`CUSOLVERRFFactorization(; symbolic = :RF, reuse_symbolic = true)`
-
-A GPU-accelerated sparse LU factorization using NVIDIA's cusolverRF library.
-This solver is specifically designed for sparse matrices on CUDA GPUs and 
-provides high-performance factorization and solve capabilities.
-
-## Keyword Arguments
-
-  - `symbolic`: The symbolic factorization method to use. Options are:
-    - `:RF` (default): Use cusolverRF's built-in symbolic analysis
-    - `:KLU`: Use KLU for symbolic analysis
-  - `reuse_symbolic`: Whether to reuse the symbolic factorization when the 
-    sparsity pattern doesn't change (default: `true`)
-
-!!! note
-    This solver requires CUSOLVERRF.jl to be loaded and only supports 
-    `Float64` element types with `Int32` indices.
-"""
-Base.@kwdef struct CUSOLVERRFFactorization <: AbstractSparseFactorization
-    symbolic::Symbol = :RF
-    reuse_symbolic::Bool = true
-end
-
-function init_cacheval(alg::CUSOLVERRFFactorization,
-        A, b, u, Pl, Pr,
-        maxiters::Int, abstol, reltol,
-        verbose::Bool, assumptions::OperatorAssumptions)
-    nothing
-end
-
-function SciMLBase.solve!(cache::LinearCache, alg::CUSOLVERRFFactorization; kwargs...)
-    error_no_cusolverrf(cache.A)
-    error("CUSOLVERRFFactorization requires CUSOLVERRF.jl to be loaded")
-end
-
-ALREADY_WARNED_CUSOLVERRF = Ref{Bool}(false)
-cusolverrf_loaded(A) = false
-
-function error_no_cusolverrf(A)
-    if LinearSolve.cusolverrf_loaded(A)
-        return nothing
-    end
-    if !ALREADY_WARNED_CUSOLVERRF[]
-        @error """
-        Attempt to use CUSOLVERRFFactorization without loading CUSOLVERRF.jl.
-        Please load the library first with `using CUSOLVERRF`.
-        """
-        ALREADY_WARNED_CUSOLVERRF[] = true
-    end
-    return nothing
-end
-
 ## SparspakFactorization is here since it's MIT licensed, not GPL
 
 """