fix sparse vector handling

Author: ChrisRackauckas

src/common.jl

@@ -98,6 +98,15 @@ function SciMLBase.init(prob::LinearProblem, alg::Union{SciMLLinearSolveAlgorith
                         kwargs...)
     @unpack A, b, u0, p = prob
 
+    A = alias_A ? A : deepcopy(A)
+    b = if b isa SparseArrays.AbstractSparseArray && !(A isa Diagonal)
+        Array(b) # the solution to a linear solve will always be dense!
+    elseif alias_b
+        b
+    else
+        deepcopy(b)
+    end
+
     u0 = if u0 !== nothing
         u0
     else
@@ -110,15 +119,6 @@ function SciMLBase.init(prob::LinearProblem, alg::Union{SciMLLinearSolveAlgorith
     isfresh = true
     Tc = typeof(cacheval)
 
-    A = alias_A ? A : deepcopy(A)
-    b = if b isa SparseArrays.AbstractSparseArray && !(A isa Diagonal)
-        Array(b) # the solution to a linear solve will always be dense!
-    elseif alias_b
-        b
-    else
-        deepcopy(b)
-    end
-
     cache = LinearCache{
                         typeof(A),
                         typeof(b),

test/runtests.jl

@@ -22,6 +22,7 @@ if GROUP == "All" || GROUP == "Core"
     @time @safetestset "Basic Tests" begin include("basictests.jl") end
     @time @safetestset "Zero Initialization Tests" begin include("zeroinittests.jl") end
     @time @safetestset "Non-Square Tests" begin include("nonsquare.jl") end
+    @time @safetestset "SparseVector b Tests" begin include("sparse_vector.jl") end
 end
 
 if GROUP == "LinearSolveCUDA"

test/sparse_vector.jl

@@ -0,0 +1,27 @@
+using SparseArrays
+using LinearSolve
+using LinearAlgebra
+
+# Constructing sparse array 
+function hess_sparse(x::Vector{T}) where T
+    return [-sin(x[1] + x[2]) + 1, -sin(x[1] + x[2]), -sin(x[1] + x[2]), -sin(x[1] + x[2]) + 1.0, 1.0, 1.0, 12.0*x[5]^2 + 1.0, 1.0]
+end
+rowval = [1, 1, 2, 2, 3, 4, 5, 6]
+colval = [1, 2, 1, 2, 3, 4, 5, 6]
+
+# Constructing sparse vec
+function grad_sparse(x::Vector{T}) where T<: Number
+    return [cos(x[1] + x[2]), cos(x[1] + x[2]), 2*x[3], 1/2, 4*x[5]^3, 1/2]
+end
+gradinds = [1, 2, 3, 4, 5, 6]
+
+# Forming the matrix and vector
+x0 = [0.7853981648713337, 0.7853981693418342, 1.023999999999997e-7, -1.0, 0.33141395338218227, -1.0]
+n = length(x0)
+hess_mat = sparse(rowval, colval, hess_sparse(x0), n, n) 
+grad_vec = sparsevec(gradinds, grad_sparse(x0), n)
+
+# # Converting grad_vec to dense succeds in solving
+prob =  LinearProblem(hess_mat, grad_vec)
+linsolve = init(prob)
+@test solve(linsolve).u ≈ hess_mat \ Array(grad_vec)