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Including jvp for radius update schemes #172

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merged 12 commits into from
Apr 3, 2023
Merged

Including jvp for radius update schemes #172

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2cfa3f1
jvp for yuan's method
yash-rs Mar 24, 2023
47fef1e
jvp modifications
yash-rs Mar 25, 2023
12b4655
fixes
yash-rs Mar 26, 2023
e251104
added some simple tests
yash-rs Mar 26, 2023
7d0e31d
more tests and jvp modifications
yash-rs Mar 26, 2023
df859ff
more tests
yash-rs Mar 29, 2023
08e34dc
fan's method
yash-rs Mar 29, 2023
230ccb1
iip issue resolved
yash-rs Mar 31, 2023
e76281b
more tests
yash-rs Mar 31, 2023
d80286d
fixed parameter issue in jvp
yash-rs Apr 2, 2023
d2553e2
Merge branch 'master' into ys/tr_jvp
ChrisRackauckas Apr 2, 2023
50f266d
tolerance decreased
yash2798 Apr 2, 2023
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2 changes: 2 additions & 0 deletions src/jacobian.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -144,3 +144,5 @@ function jacobian_autodiff(f, x::AbstractArray, nonlinfun, alg)
jac_prototype = jac_prototype, chunksize = chunk_size),
num_of_chunks)
end


41 changes: 31 additions & 10 deletions src/trustRegion.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -85,6 +85,7 @@ EnumX.@enumx RadiusUpdateSchemes begin
Hei
Yuan
Bastin
Fan
end

struct TrustRegion{CS, AD, FDT, L, P, ST, CJ, MTR} <:
Expand Down Expand Up @@ -234,7 +235,7 @@ function SciMLBase.__init(prob::NonlinearProblem{uType, iip}, alg::TrustRegion,
args...;
alias_u0 = false,
maxiters = 1000,
abstol = 1e-6,
abstol = 1e-8,
internalnorm = DEFAULT_NORM,
kwargs...) where {uType, iip}
if alias_u0
Expand Down Expand Up @@ -301,7 +302,7 @@ function SciMLBase.__init(prob::NonlinearProblem{uType, iip}, alg::TrustRegion,
p2 = convert(eltype(u), 1/6) # c5
p3 = convert(eltype(u), 6.0) # c6
p4 = convert(eltype(u), 0.0)
end
end

return TrustRegionCache{iip}(f, alg, u, fu, p, uf, linsolve, J, jac_config,
1, false, maxiters, internalnorm,
Expand Down Expand Up @@ -402,10 +403,12 @@ function trust_region_step!(cache::TrustRegionCache)
@unpack shrink_threshold, p1, p2, p3, p4 = cache
if rfunc(r, shrink_threshold, p1, p3, p4, p2) * cache.internalnorm(step_size) < cache.trust_r
cache.shrink_counter += 1
else
cache.shrink_counter = 0
end
cache.trust_r = rfunc(r, shrink_threshold, p1, p3, p4, p2) * cache.internalnorm(step_size) # parameters to be defined
cache.trust_r = rfunc(r, shrink_threshold, p1, p3, p4, p2) * cache.internalnorm(step_size)

if iszero(cache.fu) || cache.internalnorm(cache.fu) < cache.abstol || cache.internalnorm(g) < cache.ϵ # parameters to be defined
if iszero(cache.fu) || cache.internalnorm(cache.fu) < cache.abstol || cache.internalnorm(g) < cache.ϵ
cache.force_stop = true
end

Expand All @@ -416,19 +419,20 @@ function trust_region_step!(cache::TrustRegionCache)
cache.shrink_counter += 1
elseif r >= cache.expand_threshold && cache.internalnorm(step_size) > cache.trust_r / 2
cache.p1 = cache.p3 * cache.p1
cache.shrink_counter = 0
end
@unpack p1, fu, f, J = cache
#cache.trust_r = p1 * cache.internalnorm(jacobian!(J, cache) * fu) # we need the gradient at the new (k+1)th point WILL THIS BECOME ALLOCATING?


if r > cache.step_threshold
take_step!(cache)
cache.loss = cache.loss_new
cache.make_new_J = true
else
cache.make_new_J = false
end

if iszero(cache.fu) || cache.internalnorm(cache.fu) < cache.abstol || cache.internalnorm(g) < cache.ϵ # parameters to be defined

@unpack p1= cache
cache.trust_r = p1 * cache.internalnorm(jvp!(cache))
if iszero(cache.fu) || cache.internalnorm(cache.fu) < cache.abstol || cache.internalnorm(g) < cache.ϵ
cache.force_stop = true
end

Expand All @@ -441,7 +445,7 @@ function dogleg!(cache::TrustRegionCache)

# Test if the full step is within the trust region.
if norm(u_tmp) ≤ trust_r
cache.step_size = u_tmp
cache.step_size = deepcopy(u_tmp)
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Is this supposed to be a .=?

return
end

Expand Down Expand Up @@ -473,6 +477,23 @@ function take_step!(cache::TrustRegionCache{false})
cache.fu = cache.fu_new
end

function jvp!(cache::TrustRegionCache{false})
@unpack f, u, fu, p = cache
if isa(u, Number)
return value_derivative(x -> f(x, p), u)
end
return auto_jacvec(x -> f(x, p), u, fu)
end

function jvp!(cache::TrustRegionCache{true})
@unpack g, f, u, fu, p = cache
if isa(u, Number)
return value_derivative(x -> f(x, p), u)
end
return auto_jacvec!(g, (fu, x) -> f(fu, x, p), u, fu)
g
end

function SciMLBase.solve!(cache::TrustRegionCache)
while !cache.force_stop && cache.iter < cache.maxiters &&
cache.shrink_counter < cache.alg.max_shrink_times
Expand Down
127 changes: 99 additions & 28 deletions test/basictests.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -163,61 +163,69 @@ end

# --- TrustRegion tests ---

function benchmark_immutable(f, u0)
function benchmark_immutable(f, u0, radius_update_scheme)
probN = NonlinearProblem{false}(f, u0)
solver = init(probN, TrustRegion(), abstol = 1e-9)
solver = init(probN, TrustRegion(radius_update_scheme = radius_update_scheme), abstol = 1e-9)
sol = solve!(solver)
end

function benchmark_mutable(f, u0)
function benchmark_mutable(f, u0, radius_update_scheme)
probN = NonlinearProblem{false}(f, u0)
solver = init(probN, TrustRegion(), abstol = 1e-9)
solver = init(probN, TrustRegion(radius_update_scheme = radius_update_scheme), abstol = 1e-9)
sol = solve!(solver)
end

function benchmark_scalar(f, u0)
function benchmark_scalar(f, u0, radius_update_scheme)
probN = NonlinearProblem{false}(f, u0)
sol = (solve(probN, TrustRegion(), abstol = 1e-9))
sol = (solve(probN, TrustRegion(radius_update_scheme = radius_update_scheme), abstol = 1e-9))
end

function ff(u, p)
function ff(u, p=nothing)
u .* u .- 2
end

function sf(u, p)
function sf(u, p=nothing)
u * u - 2
end

u0 = [1.0, 1.0]
radius_update_schemes = [RadiusUpdateSchemes.Simple, RadiusUpdateSchemes.Hei, RadiusUpdateSchemes.Yuan]

sol = benchmark_immutable(ff, cu0)
@test sol.retcode === ReturnCode.Success
@test all(abs.(sol.u .* sol.u .- 2) .< 1e-9)
sol = benchmark_mutable(ff, u0)
@test sol.retcode === ReturnCode.Success
@test all(abs.(sol.u .* sol.u .- 2) .< 1e-9)
sol = benchmark_scalar(sf, csu0)
@test sol.retcode === ReturnCode.Success
@test abs(sol.u * sol.u - 2) < 1e-9
for radius_update_scheme in radius_update_schemes
sol = benchmark_immutable(ff, cu0, radius_update_scheme)
@test sol.retcode === ReturnCode.Success
@test all(abs.(sol.u .* sol.u .- 2) .< 1e-9)
sol = benchmark_mutable(ff, u0, radius_update_scheme)
@test sol.retcode === ReturnCode.Success
@test all(abs.(sol.u .* sol.u .- 2) .< 1e-9)
sol = benchmark_scalar(sf, csu0, radius_update_scheme)
@test sol.retcode === ReturnCode.Success
@test abs(sol.u * sol.u - 2) < 1e-9
end

function benchmark_inplace(f, u0)

function benchmark_inplace(f, u0, radius_update_scheme)
probN = NonlinearProblem{true}(f, u0)
solver = init(probN, TrustRegion(), abstol = 1e-9)
solver = init(probN, TrustRegion(radius_update_scheme = radius_update_scheme), abstol = 1e-9)
sol = solve!(solver)
end

function ffiip(du, u, p)
function ffiip(du, u, p=nothing)
du .= u .* u .- 2
end
u0 = [1.0, 1.0]

sol = benchmark_inplace(ffiip, u0)
@test sol.retcode === ReturnCode.Success
@test all(abs.(sol.u .* sol.u .- 2) .< 1e-9)
for radius_update_scheme in radius_update_schemes
sol = benchmark_inplace(ffiip, u0, radius_update_scheme)
@test sol.retcode === ReturnCode.Success
@test all(abs.(sol.u .* sol.u .- 2) .< 1e-9)
end

u0 = [1.0, 1.0]
probN = NonlinearProblem{true}(ffiip, u0)
solver = init(probN, TrustRegion(), abstol = 1e-9)
@test (@ballocated solve!(solver)) < 200
for radius_update_scheme in radius_update_schemes
probN = NonlinearProblem{true}(ffiip, u0)
solver = init(probN, TrustRegion(radius_update_scheme = radius_update_scheme), abstol = 1e-9)
@test (@ballocated solve!(solver)) < 200
end

# AD Tests
using ForwardDiff
Expand All @@ -236,6 +244,29 @@ for p in 1.1:0.1:100.0
@test ForwardDiff.derivative(g, p) ≈ 1 / (2 * sqrt(p))
end

g = function (p)
probN = NonlinearProblem{false}(f, csu0, p)
sol = solve(probN, TrustRegion(radius_update_scheme = RadiusUpdateSchemes.Hei), abstol = 1e-9)
return sol.u[end]
end

for p in 1.1:0.1:100.0
@test g(p) ≈ sqrt(p)
@test ForwardDiff.derivative(g, p) ≈ 1 / (2 * sqrt(p))
end

## FAIL BECAUSE JVP CANNOT ACCEPT PARAMETERS IN FUNCTIONS
g = function (p)
probN = NonlinearProblem{false}(f, csu0, p)
sol = solve(probN, TrustRegion(radius_update_scheme = RadiusUpdateSchemes.Yuan), abstol = 1e-9)
return sol.u[end]
end

for p in 1.1:0.1:100.0
@test g(p) ≈ sqrt(p)
@test ForwardDiff.derivative(g, p) ≈ 1 / (2 * sqrt(p))
end

# Scalar
f, u0 = (u, p) -> u * u - p, 1.0

Expand All @@ -252,6 +283,32 @@ for p in 1.1:0.1:100.0
@test ForwardDiff.derivative(g, p) ≈ 1 / (2 * sqrt(p))
end

g = function (p)
probN = NonlinearProblem{false}(f, oftype(p, u0), p)
sol = solve(probN, TrustRegion(radius_update_scheme = RadiusUpdateSchemes.Hei), abstol = 1e-10)
return sol.u
end

@test ForwardDiff.derivative(g, 3.0) ≈ 1 / (2 * sqrt(3.0))

for p in 1.1:0.1:100.0
@test g(p) ≈ sqrt(p)
@test ForwardDiff.derivative(g, p) ≈ 1 / (2 * sqrt(p))
end

g = function (p)
probN = NonlinearProblem{false}(f, oftype(p, u0), p)
sol = solve(probN, TrustRegion(radius_update_scheme = RadiusUpdateSchemes.Yuan), abstol = 1e-10)
return sol.u
end

@test ForwardDiff.derivative(g, 3.0) ≈ 1 / (2 * sqrt(3.0))

for p in 1.1:0.1:100.0
@test g(p) ≈ sqrt(p)
@test ForwardDiff.derivative(g, p) ≈ 1 / (2 * sqrt(p))
end

f = (u, p) -> p[1] * u * u - p[2]
t = (p) -> [sqrt(p[2] / p[1])]
p = [0.9, 50.0]
Expand All @@ -263,6 +320,14 @@ end
@test gnewton(p) ≈ [sqrt(p[2] / p[1])]
@test ForwardDiff.jacobian(gnewton, p) ≈ ForwardDiff.jacobian(t, p)

gnewton = function (p)
probN = NonlinearProblem{false}(f, 0.5, p)
sol = solve(probN, TrustRegion(radius_update_scheme = RadiusUpdateSchemes.Hei))
return [sol.u]
end
@test gnewton(p) ≈ [sqrt(p[2] / p[1])]
@test ForwardDiff.jacobian(gnewton, p) ≈ ForwardDiff.jacobian(t, p)

# Iterator interface
f = (u, p) -> u * u - p
g = function (p_range)
Expand Down Expand Up @@ -295,12 +360,18 @@ p = range(0.01, 2, length = 200)
@test g(p) ≈ sqrt.(p)

# Error Checks
f, u0 = (u, p) -> u .* u .- 2.0, @SVector[1.0, 1.0]
f, u0 = (u, p) -> u .* u .- 2, @SVector[1.0, 1.0]
probN = NonlinearProblem(f, u0)

@test solve(probN, TrustRegion()).u[end] ≈ sqrt(2.0)
@test solve(probN, TrustRegion(; autodiff = false)).u[end] ≈ sqrt(2.0)

@test solve(probN, TrustRegion(radius_update_scheme = RadiusUpdateSchemes.Hei)).u[end] ≈ sqrt(2.0)
@test solve(probN, TrustRegion(; radius_update_scheme = RadiusUpdateSchemes.Hei, autodiff = false)).u[end] ≈ sqrt(2.0)

@test solve(probN, TrustRegion(radius_update_scheme = RadiusUpdateSchemes.Yuan)).u[end] ≈ sqrt(2.0)
@test solve(probN, TrustRegion(; radius_update_scheme = RadiusUpdateSchemes.Yuan, autodiff = false)).u[end] ≈ sqrt(2.0)

for u0 in [1.0, [1, 1.0]]
local f, probN, sol
f = (u, p) -> u .* u .- 2.0
Expand Down