format test

Author: ChrisRackauckas

test/sparse_vector.jl

@@ -3,25 +3,41 @@ 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]
+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]
+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]
+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) 
+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)
+prob = LinearProblem(hess_mat, grad_vec)
 linsolve = init(prob)
 @test solve(linsolve).u ≈ hess_mat \ Array(grad_vec)