Improve interface

Project.toml

@@ -9,12 +9,12 @@ FastLapackInterface = "29a986be-02c6-4525-aec4-84b980013641"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 NLSolversBase = "d41bc354-129a-5804-8e4c-c37616107c6c"
 PositiveFactorizations = "85a6dd25-e78a-55b7-8502-1745935b8125"
-Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
 PrecompileTools = "aea7be01-6a6a-4083-8856-8a6e6704d82a"
+Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
 
 [compat]
 CommonSolve = "0.2"
-FastLapackInterface = "1.2"
+FastLapackInterface = "1.2, 2"
 NLSolversBase = "7.6"
 PositiveFactorizations = "0.2.4"
 PrecompileTools = "1"

src/NonlinearSystems.jl

@@ -5,7 +5,7 @@ using CommonSolve: solve
 using FastLapackInterface: LUWs
 using LinearAlgebra: BLAS, LAPACK, LU, Cholesky, cholesky!, Hermitian, ldiv!, mul!,
     lowrankupdate!
-using NLSolversBase: OnceDifferentiable, value_jacobian!!, jacobian!!
+using NLSolversBase: OnceDifferentiable, value_jacobian!!, value!!, jacobian!!
 using PositiveFactorizations
 using Printf
 

src/hybrid.jl

@@ -53,6 +53,7 @@ struct HybridSolver{T, L, V} <: AbstractSolver{T}
     thres_jac::Int
     thres_nslow1::Int
     thres_nslow2::Int
+    warn::Bool
 end
 
 """
@@ -79,13 +80,15 @@ See also [`Hybrid`](@ref).
 - `thres_nslow2::Integer=5`: signal slow solver progress
   if there is no expansion of trust region after recomputing the Jacobian matrix
   in the specified number of consecutive steps.
+- `warn::Bool=true`: print a warning message for slow solver progress
 """
 function HybridSolver(fdf::OnceDifferentiable, x::AbstractVector, fx::AbstractVector,
         J::AbstractMatrix, P::Type{<:ProblemType};
         linsolver=default_linsolver(fdf, x0, P),
         factor_init::Real=1.0, factor_up::Real=2.0, factor_down::Real=0.5,
         scaling::Bool=true, rank1update::Bool=true,
-        thres_jac::Integer=2, thres_nslow1::Integer=10, thres_nslow2::Integer=5)
+        thres_jac::Integer=2, thres_nslow1::Integer=10, thres_nslow2::Integer=5,
+        warn::Bool=true)
     M, N = size(J) # Assume the sizes of x, fx and J are all compatible
     P === RootFinding && M != N && throw(DimensionMismatch(
         "the number of variables must match the number of equations in a root-finding problem"))
@@ -114,7 +117,7 @@ function HybridSolver(fdf::OnceDifferentiable, x::AbstractVector, fx::AbstractVe
     return HybridSolver(Ref(state), linsolver, diagn,
         newton, grad, df, Jdx, w, v, factor_init, factor_up, factor_down,
         scaling, rank1update, convert(Int, thres_jac),
-        convert(Int, thres_nslow1), convert(Int, thres_nslow2))
+        convert(Int, thres_nslow1), convert(Int, thres_nslow2), warn)
 end
 
 # ! This method reuses arrays from s
@@ -123,13 +126,15 @@ function init(s::NonlinearSystem{P,V,M,<:HybridSolver{T}}, x0::V;
         factor_down::Real=s.solver.factor_down, scaling::Bool=s.solver.scaling,
         rank1update::Bool=s.solver.rank1update, thres_jac::Integer=s.solver.thres_jac,
         thres_nslow1::Integer=s.solver.thres_nslow1,
-        thres_nslow2::Integer=s.solver.thres_nslow2, kwargs...) where {P,V,M,T}
-    copyto!(s.x, x0)
+        thres_nslow2::Integer=s.solver.thres_nslow2,
+        warn::Bool=s.solver.warn,
+        initf::Bool=true, initdf::Bool=true, kwargs...) where {P,V,M,T}
+    s.x === x0 || copyto!(s.x, x0)
     nan = convert(eltype(s.x), NaN)
     fill!(s.dx, nan)
     ss, fdf = s.solver, s.fdf
     linsolver = getlinsolver(s)
-    init(linsolver, fdf, s.x)
+    init(linsolver, fdf, s.x; initf=initf, initdf=initdf)
     fill!(ss.grad, nan)
     copyto!(s.fx, fdf.F)
     diagn = ss.diagn
@@ -146,8 +151,10 @@ function init(s::NonlinearSystem{P,V,M,<:HybridSolver{T}}, x0::V;
     solver = HybridSolver(Ref(state), linsolver, diagn,
         ss.newton, ss.grad, ss.df, ss.Jdx, ss.w, ss.v, factor_init, factor_up, factor_down,
         scaling, rank1update, convert(Int, thres_jac),
-        convert(Int, thres_nslow1), convert(Int, thres_nslow2))
-    return NonlinearSystem(P, s.fdf, s.x, s.fx, s.dx, solver; kwargs...)
+        convert(Int, thres_nslow1), convert(Int, thres_nslow2), warn)
+    return NonlinearSystem(P, s.fdf, s.x, s.fx, s.dx, solver;
+        lower=s.lb, upper=s.ub, maxiter=s.maxiter, ftol=s.ftol, gtol=s.gtol,
+        xtol=s.xtol, xtolr=s.xtolr, showtrace=s.showtrace, kwargs...)
 end
 
 function dogleg!(dx, linsolver, J, fx, diagn, δ, newton, grad, w)
@@ -291,10 +298,10 @@ function (s::HybridSolver{T})(fdf::OnceDifferentiable, x::AbstractVector,
 
     if nslow1 === s.thres_nslow1
         if nslow2 >= s.thres_nslow2
-            @warn "iteration $(iter) is not making progress even with reevaluations of Jacobians; try a smaller value with option thres_jac"
+            s.warn && @warn "iteration $(iter) is not making progress even with reevaluations of Jacobians; try a smaller value with option thres_jac"
             return iter, jac_noprogress
         else
-            @warn "iteration $(iter) is not making progress"
+            s.warn && @warn "iteration $(iter) is not making progress"
             return iter, eval_noprogress
         end
     else
@@ -306,15 +313,15 @@ function init(::Type{Hybrid{P}}, fdf::OnceDifferentiable, x0::AbstractVector;
         linsolver=default_linsolver(fdf, x0, P),
         factor_init::Real=1.0, factor_up::Real=2.0, factor_down::Real=0.5,
         scaling=true, rank1update=true,
-        thres_jac=2, thres_nslow1=10, thres_nslow2=5, kwargs...) where P
+        thres_jac=2, thres_nslow1=10, thres_nslow2=5, warn=true, kwargs...) where P
     x = copy(x0)
     fx = copy(fdf.F)
     dx = similar(x) # Preserve the array type
     fill!(dx, convert(eltype(x), NaN))
     solver = HybridSolver(fdf, x, fx, fdf.DF, P; linsolver=linsolver,
         factor_init=factor_init, factor_up=factor_up, factor_down=factor_down,
         scaling=scaling, rank1update=rank1update,
-        thres_jac=thres_jac, thres_nslow1=thres_nslow1, thres_nslow2=thres_nslow2)
+        thres_jac=thres_jac, thres_nslow1=thres_nslow1, thres_nslow2=thres_nslow2, warn=warn)
     # Remaining kwargs are handled by NonlinearSystem constructor
     return NonlinearSystem(P, fdf, x, fx, dx, solver; kwargs...)
 end

src/interface.jl

@@ -168,11 +168,13 @@ solve!(s::NonlinearSystem, x0; kwargs...) = solve!(init(s, x0; kwargs...))
 init(algo::AbstractAlgorithm, args...; kwargs...) =
     init(typeof(algo), args...; kwargs...)
 
-init(Algo::Type{<:AbstractAlgorithm}, f, x0::AbstractVector; kwargs...) =
-    init(Algo, OnceDifferentiable(f, similar(x0), similar(x0)), x0; kwargs...)
+init(Algo::Type{<:AbstractAlgorithm}, f, x0::AbstractVector, nf::Int=length(x0);
+    kwargs...) =
+        init(Algo, OnceDifferentiable(f, similar(x0), similar(x0, nf)), x0; kwargs...)
 
-init(Algo::Type{<:AbstractAlgorithm}, f, j, x0::AbstractVector; kwargs...) =
-    init(Algo, OnceDifferentiable(f, j, similar(x0), similar(x0)), x0; kwargs...)
+init(Algo::Type{<:AbstractAlgorithm}, f, j, x0::AbstractVector, nf::Int=length(x0);
+    kwargs...) =
+        init(Algo, OnceDifferentiable(f, j, similar(x0), similar(x0, nf)), x0; kwargs...)
 
 function show(io::IO, s::NonlinearSystem{P}) where P
     print(io, nequ(s), '×', nvar(s), ' ', typeof(s).name.name, '{', P, "}(")

src/linsolve.jl

@@ -43,8 +43,9 @@ default_linsolver(fdf::OnceDifferentiable{V, M, V}, x0::V,
     ::Type{RootFinding}) where {V<:AbstractVector, M<:AbstractMatrix} =
         init(DenseLUSolver, fdf, x0)
 
-init(::Type{DenseLUSolver}, fdf::OnceDifferentiable, x0::AbstractVector) =
-    (_init!(fdf, x0); init(DenseLUSolver, fdf.DF, fdf.F))
+init(::Type{DenseLUSolver}, fdf::OnceDifferentiable, x0::AbstractVector;
+    initf::Bool=true, initdf::Bool=true) =
+        (_init!(fdf, x0, initf, initdf); init(DenseLUSolver, fdf.DF, fdf.F))
 
 function init(::Type{DenseLUSolver}, J::AbstractMatrix, f::AbstractVector)
     ws = LUWs(J)
@@ -53,8 +54,9 @@ function init(::Type{DenseLUSolver}, J::AbstractMatrix, f::AbstractVector)
     return DenseLUSolver(ws, fac)
 end
 
-init(s::DenseLUSolver, fdf::OnceDifferentiable, x0::AbstractVector) =
-    (_init!(fdf, x0); update!(s, fdf.DF); s)
+init(s::DenseLUSolver, fdf::OnceDifferentiable, x0::AbstractVector;
+    initf::Bool=true, initdf::Bool=true) =
+        (_init!(fdf, x0, initf, initdf); update!(s, fdf.DF); s)
 
 update!(s::DenseLUSolver{<:LU}, J::AbstractMatrix) =
     (s.fac = LU(LAPACK.getrf!(s.ws, copyto!(getfield(s.fac, :factors), J))...); nothing)
@@ -144,8 +146,9 @@ default_linsolver(fdf::OnceDifferentiable{V, M, V}, x0::V,
     ::Type{LeastSquares}) where {V<:AbstractVector, M<:AbstractMatrix} =
         init(DenseCholeskySolver, fdf, x0)
 
-init(::Type{DenseCholeskySolver}, fdf::OnceDifferentiable, x0::AbstractVector; kwargs...) =
-    (_init!(fdf, x0); init(DenseCholeskySolver, fdf.DF, fdf.F; kwargs...))
+init(::Type{DenseCholeskySolver}, fdf::OnceDifferentiable, x0::AbstractVector;
+    initf::Bool=true, initdf::Bool=true, kwargs...) =
+        (_init!(fdf, x0, initf, initdf); init(DenseCholeskySolver, fdf.DF, fdf.F; kwargs...))
 
 function init(::Type{DenseCholeskySolver}, J::AbstractMatrix, f::AbstractVector;
         rank1chol::Bool=length(f)>3)
@@ -160,8 +163,9 @@ function init(::Type{DenseCholeskySolver}, J::AbstractMatrix, f::AbstractVector;
     end
 end
 
-init(s::DenseCholeskySolver, fdf::OnceDifferentiable{V, M, V}, x0::V) where {V, M} =
-    (_init!(fdf, x0); update!(s, fdf.DF); s)
+init(s::DenseCholeskySolver, fdf::OnceDifferentiable{V, M, V}, x0::V;
+    initf::Bool=true, initdf::Bool=true) where {V, M} =
+        (_init!(fdf, x0, initf, initdf); update!(s, fdf.DF); s)
 
 update!(s::DenseCholeskySolver, J::AbstractMatrix) =
     (s.fac = _cholesky!(mul!(s.JtJ, J', J), s.d); return nothing)

src/utils.jl

@@ -25,10 +25,16 @@ end
     return sqrt(e2)
 end
 
-function _init!(fdf::OnceDifferentiable, x0)
+function _init!(fdf::OnceDifferentiable, x0, initf::Bool, initdf::Bool)
     fdf.f_calls[1] = 0
     fdf.df_calls[1] = 0
-    value_jacobian!!(fdf, x0)
+    if initf && initdf
+        value_jacobian!!(fdf, x0)
+    elseif initf
+        value!!(fdf, x0)
+    elseif initdf
+        jacobian!!(fdf, x0)
+    end
     return fdf
 end
 

test/linsolve.jl

@@ -33,6 +33,21 @@ end
     s = default_linsolver(fdf, x0, RootFinding)
     Y = zeros(2)
     @test solve!(s, Y, copy(fdf.DF), copy(fdf.F)) ≈ fdf.DF \ fdf.F
+
+    x = rand(2)
+    init(s, fdf, x)
+    F = zeros(2)
+    J = zeros(2, 2)
+    @test fdf.F == f!(F, x)
+    @test fdf.DF == j!(J, x)
+    x1 = rand(2)
+    init(s, fdf, x1; initf=false)
+    @test fdf.F == f!(F, x)
+    @test fdf.DF == j!(J, x1)
+    x2 = rand(2)
+    init(s, fdf, x2; initdf=false)
+    @test fdf.F == f!(F, x2)
+    @test fdf.DF == j!(J, x1)
 end
 
 @testset "DenseCholeskySolver" begin
@@ -60,4 +75,19 @@ end
     update!(s1, copy(J), copy(w), copy(v))
     J1 = J .+ w .* v'
     @test s1.fac.U's1.fac.U ≈ J1'J1
+
+    x = rand(2)
+    init(s, fdf, x)
+    F = zeros(2)
+    J = zeros(2, 2)
+    @test fdf.F == f!(F, x)
+    @test fdf.DF == j!(J, x)
+    x1 = rand(2)
+    init(s, fdf, x1; initf=false)
+    @test fdf.F == f!(F, x)
+    @test fdf.DF == j!(J, x1)
+    x2 = rand(2)
+    init(s, fdf, x2; initdf=false)
+    @test fdf.F == f!(F, x2)
+    @test fdf.DF == j!(J, x1)
 end