format

Author: ChrisRackauckas

lib/SCCNonlinearSolve/src/SCCNonlinearSolve.jl

@@ -12,8 +12,9 @@ Algorithm for solving Strongly Connected Component (SCC) problems containing
 both nonlinear and linear subproblems.
 
 ### Keyword Arguments
-- `nlalg`: Algorithm to use for solving NonlinearProblem components
-- `linalg`: Algorithm to use for solving LinearProblem components
+
+  - `nlalg`: Algorithm to use for solving NonlinearProblem components
+  - `linalg`: Algorithm to use for solving LinearProblem components
 """
 struct SCCAlg{N, L}
     nlalg::N
@@ -34,7 +35,7 @@ iteratively_build_sols(alg, sols; kwargs...) = sols
 function iteratively_build_sols(alg, sols, (prob, explicitfun), args...; kwargs...)
     explicitfun(
         SymbolicIndexingInterface.parameter_values(prob), sols)
-    
+
     _sol = if prob isa SciMLBase.LinearProblem
         sol = SciMLBase.solve(prob, alg.linalg; kwargs...)
         SciMLBase.build_linear_solution(
@@ -50,7 +51,8 @@ end
 
 function CommonSolve.solve(prob::SciMLBase.SCCNonlinearProblem, alg::SCCAlg; kwargs...)
     numscc = length(prob.probs)
-    sols = iteratively_build_sols(alg, (), zip(prob.probs, prob.explicitfuns!)...; kwargs...) 
+    sols = iteratively_build_sols(
+        alg, (), zip(prob.probs, prob.explicitfuns!)...; kwargs...)
 
     # TODO: fix allocations with a lazy concatenation
     u = reduce(vcat, sols)
@@ -66,5 +68,4 @@ function CommonSolve.solve(prob::SciMLBase.SCCNonlinearProblem, alg::SCCAlg; kwa
     SciMLBase.build_solution(prob, alg, u, resid; retcode, original = sols)
 end
 
-
 end

lib/SCCNonlinearSolve/test/core_tests.jl

@@ -7,67 +7,67 @@ end
 @testitem "Manual SCC" setup=[CoreRootfindTesting] tags=[:core] begin
     using NonlinearSolveFirstOrder
     function f(du, u, p)
-        du[1] = cos(u[2]) - u[1]
-        du[2] = sin(u[1] + u[2]) + u[2]
-        du[3] = 2u[4] + u[3] + 1.0
-        du[4] = u[5]^2 + u[4]
-        du[5] = u[3]^2 + u[5]
-        du[6] = u[1] + u[2] + u[3] + u[4] + u[5] + 2.0u[6] + 2.5u[7] + 1.5u[8]
-        du[7] = u[1] + u[2] + u[3] + 2.0u[4] + u[5] + 4.0u[6] - 1.5u[7] + 1.5u[8]
-        du[8] = u[1] + 2.0u[2] + 3.0u[3] + 5.0u[4] + 6.0u[5] + u[6] - u[7] - u[8]
+        du[1]=cos(u[2])-u[1]
+        du[2]=sin(u[1]+u[2])+u[2]
+        du[3]=2u[4]+u[3]+1.0
+        du[4]=u[5]^2+u[4]
+        du[5]=u[3]^2+u[5]
+        du[6]=u[1]+u[2]+u[3]+u[4]+u[5]+2.0u[6]+2.5u[7]+1.5u[8]
+        du[7]=u[1]+u[2]+u[3]+2.0u[4]+u[5]+4.0u[6]-1.5u[7]+1.5u[8]
+        du[8]=u[1]+2.0u[2]+3.0u[3]+5.0u[4]+6.0u[5]+u[6]-u[7]-u[8]
     end
-    prob = NonlinearProblem(f, zeros(8))
-    sol = solve(prob, NewtonRaphson())
+    prob=NonlinearProblem(f, zeros(8))
+    sol=solve(prob, NewtonRaphson())
 
-    u0 = zeros(2)
-    p = zeros(3)
+    u0=zeros(2)
+    p=zeros(3)
 
     function f1(du, u, p)
-        du[1] = cos(u[2]) - u[1]
-        du[2] = sin(u[1] + u[2]) + u[2]
+        du[1]=cos(u[2])-u[1]
+        du[2]=sin(u[1]+u[2])+u[2]
     end
-    explicitfun1(p, sols) = nothing
-    prob1 = NonlinearProblem(
+    explicitfun1(p, sols)=nothing
+    prob1=NonlinearProblem(
         NonlinearFunction{true, SciMLBase.NoSpecialize}(f1), zeros(2), p)
-    sol1 = solve(prob1, NewtonRaphson())
+    sol1=solve(prob1, NewtonRaphson())
 
     function f2(du, u, p)
-        du[1] = 2u[2] + u[1] + 1.0
-        du[2] = u[3]^2 + u[2]
-        du[3] = u[1]^2 + u[3]
+        du[1]=2u[2]+u[1]+1.0
+        du[2]=u[3]^2+u[2]
+        du[3]=u[1]^2+u[3]
     end
-    explicitfun2(p, sols) = nothing
-    prob2 = NonlinearProblem(
+    explicitfun2(p, sols)=nothing
+    prob2=NonlinearProblem(
         NonlinearFunction{true, SciMLBase.NoSpecialize}(f2), zeros(3), p)
-    sol2 = solve(prob2, NewtonRaphson())
+    sol2=solve(prob2, NewtonRaphson())
 
     # Convert f3 to a LinearProblem since it's linear in u
     # du = Au + b where A is the coefficient matrix and b is from parameters
-    A3 = [2.0 2.5 1.5; 4.0 -1.5 1.5; 1.0 -1.0 -1.0]
-    b3 = p  # b will be updated by explicitfun3
-    prob3 = LinearProblem(A3, b3, zeros(3))
+    A3=[2.0 2.5 1.5; 4.0 -1.5 1.5; 1.0 -1.0 -1.0]
+    b3=p  # b will be updated by explicitfun3
+    prob3=LinearProblem(A3, b3, zeros(3))
     function explicitfun3(p, sols)
-        p[1] = -(sols[1][1] + sols[1][2] + sols[2][1] + sols[2][2] + sols[2][3])
-        p[2] = -(sols[1][1] + sols[1][2] + sols[2][1] + 2.0sols[2][2] + sols[2][3])
-        p[3] = -(sols[1][1] + 2.0sols[1][2] + 3.0sols[2][1] + 5.0sols[2][2] +
+        p[1]=-(sols[1][1]+sols[1][2]+sols[2][1]+sols[2][2]+sols[2][3])
+        p[2]=-(sols[1][1]+sols[1][2]+sols[2][1]+2.0sols[2][2]+sols[2][3])
+        p[3]=-(sols[1][1]+2.0sols[1][2]+3.0sols[2][1]+5.0sols[2][2]+
                6.0sols[2][3])
     end
     explicitfun3(p, [sol1, sol2])
-    sol3 = solve(prob3)  # LinearProblem uses default linear solver
-    manualscc = reduce(vcat,(sol1, sol2, sol3))
+    sol3=solve(prob3)  # LinearProblem uses default linear solver
+    manualscc=reduce(vcat, (sol1, sol2, sol3))
 
-    sccprob = SciMLBase.SCCNonlinearProblem((prob1, prob2, prob3),
+    sccprob=SciMLBase.SCCNonlinearProblem((prob1, prob2, prob3),
         SciMLBase.Void{Any}.([explicitfun1, explicitfun2, explicitfun3]))
-    
+
     # Test with SCCAlg that handles both nonlinear and linear problems
     using SCCNonlinearSolve
-    scc_alg = SCCNonlinearSolve.SCCAlg(nlalg = NewtonRaphson(), linalg = nothing)
-    scc_sol = solve(sccprob, scc_alg)
+    scc_alg=SCCNonlinearSolve.SCCAlg(nlalg = NewtonRaphson(), linalg = nothing)
+    scc_sol=solve(sccprob, scc_alg)
     @test sol ≈ manualscc ≈ scc_sol
 
     import NonlinearSolve # Required for Default
 
     # Test default interface
-    scc_sol_default = solve(sccprob)
+    scc_sol_default=solve(sccprob)
     @test sol ≈ manualscc ≈ scc_sol_default
 end