Fix CUDA functional check in init_cacheval functions (#770)

- Add CUDA.functional() check before creating CUDA arrays in init_cacheval
- Return nothing if CUDA is loaded but not functional
- Fixes issue where CudaOffloadLUFactorization and CudaOffloadQRFactorization
  would fail during cache initialization when CUDA is in environment but not usable
- Addresses issue #761

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-authored-by: ChrisRackauckas <accounts@chrisrackauckas.com>
Co-authored-by: Claude <noreply@anthropic.com>

Author: 3 people

ext/LinearSolveCUDAExt.jl

@@ -53,6 +53,11 @@ end
 function LinearSolve.init_cacheval(alg::CudaOffloadLUFactorization, A::AbstractArray, b, u, Pl, Pr,
         maxiters::Int, abstol, reltol, verbose::Bool,
         assumptions::OperatorAssumptions)
+    # Check if CUDA is functional before creating CUDA arrays
+    if !CUDA.functional()
+        return nothing
+    end
+    
     T = eltype(A)
     noUnitT = typeof(zero(T))
     luT = LinearAlgebra.lutype(noUnitT)
@@ -76,6 +81,11 @@ end
 function LinearSolve.init_cacheval(alg::CudaOffloadQRFactorization, A, b, u, Pl, Pr,
         maxiters::Int, abstol, reltol, verbose::Bool,
         assumptions::OperatorAssumptions)
+    # Check if CUDA is functional before creating CUDA arrays
+    if !CUDA.functional()
+        return nothing
+    end
+    
     qr(CUDA.CuArray(A))
 end