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Improve the OptimizationManopt.jl interface #1009

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a074422
Starts adapting and reworking to Manopt 0.5, Manifolds 0.10, Manifold…
kellertuer Sep 12, 2025
a064672
fix a few tests,
kellertuer Sep 12, 2025
98b43be
Move all tests to the allocating case default.
kellertuer Sep 12, 2025
dbddd25
Collect a few comments on why and where currently tests still fail.
kellertuer Sep 12, 2025
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8 changes: 4 additions & 4 deletions lib/OptimizationManopt/Project.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -14,10 +14,10 @@ Reexport = "189a3867-3050-52da-a836-e630ba90ab69"

[compat]
LinearAlgebra = "1.10"
ManifoldDiff = "0.3.10"
Manifolds = "0.9.18"
ManifoldsBase = "0.15.10"
Manopt = "0.4.63"
ManifoldDiff = "0.4"
Manifolds = "0.10"
ManifoldsBase = "1"
Manopt = "0.5"
Optimization = "4.4"
Reexport = "1.2"
julia = "1.10"
Expand Down
163 changes: 34 additions & 129 deletions lib/OptimizationManopt/src/OptimizationManopt.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -65,20 +65,14 @@ function call_manopt_optimizer(
loss,
gradF,
x0;
stopping_criterion::Union{Manopt.StoppingCriterion, Manopt.StoppingCriterionSet},
evaluation::AbstractEvaluationType = Manopt.AllocatingEvaluation(),
stepsize::Stepsize = ArmijoLinesearch(M),
kwargs...)
opts = gradient_descent(M,
opts = Manopt.gradient_descent(M,
loss,
gradF,
x0;
return_state = true,
evaluation,
stepsize,
stopping_criterion,
kwargs...)
# we unwrap DebugOptions here
return_state = true, # return the (full, decorated) solver state
kwargs...
)
minimizer = Manopt.get_solver_result(opts)
return (; minimizer = minimizer, minimum = loss(M, minimizer), options = opts)
end
Expand All @@ -90,13 +84,8 @@ function call_manopt_optimizer(M::ManifoldsBase.AbstractManifold, opt::NelderMea
loss,
gradF,
x0;
stopping_criterion::Union{Manopt.StoppingCriterion, Manopt.StoppingCriterionSet},
kwargs...)
opts = NelderMead(M,
loss;
return_state = true,
stopping_criterion,
kwargs...)
opts = NelderMead(M, loss; return_state = true, kwargs...)
minimizer = Manopt.get_solver_result(opts)
return (; minimizer = minimizer, minimum = loss(M, minimizer), options = opts)
end
Expand All @@ -109,19 +98,14 @@ function call_manopt_optimizer(M::ManifoldsBase.AbstractManifold,
loss,
gradF,
x0;
stopping_criterion::Union{Manopt.StoppingCriterion, Manopt.StoppingCriterionSet},
evaluation::AbstractEvaluationType = InplaceEvaluation(),
stepsize::Stepsize = ArmijoLinesearch(M),
kwargs...)
opts = conjugate_gradient_descent(M,
opts = Manopt.conjugate_gradient_descent(M,
loss,
gradF,
x0;
return_state = true,
evaluation,
stepsize,
stopping_criterion,
kwargs...)
kwargs...
)
# we unwrap DebugOptions here
minimizer = Manopt.get_solver_result(opts)
return (; minimizer = minimizer, minimum = loss(M, minimizer), options = opts)
Expand All @@ -135,25 +119,10 @@ function call_manopt_optimizer(M::ManifoldsBase.AbstractManifold,
loss,
gradF,
x0;
stopping_criterion::Union{Manopt.StoppingCriterion, Manopt.StoppingCriterionSet},
evaluation::AbstractEvaluationType = InplaceEvaluation(),
population_size::Int = 100,
retraction_method::AbstractRetractionMethod = default_retraction_method(M),
inverse_retraction_method::AbstractInverseRetractionMethod = default_inverse_retraction_method(M),
vector_transport_method::AbstractVectorTransportMethod = default_vector_transport_method(M),
kwargs...)
initial_population = vcat([x0], [rand(M) for _ in 1:(population_size - 1)])
opts = particle_swarm(M,
loss;
x0 = initial_population,
n = population_size,
return_state = true,
retraction_method,
inverse_retraction_method,
vector_transport_method,
stopping_criterion,
kwargs...)
# we unwrap DebugOptions here
swarm = [x0, [rand(M) for _ in 1:(population_size - 1)]...]
opts = particle_swarm(M, loss, swarm; return_state = true, kwargs...)
minimizer = Manopt.get_solver_result(opts)
return (; minimizer = minimizer, minimum = loss(M, minimizer), options = opts)
end
Expand All @@ -167,27 +136,9 @@ function call_manopt_optimizer(M::Manopt.AbstractManifold,
loss,
gradF,
x0;
stopping_criterion::Union{Manopt.StoppingCriterion, Manopt.StoppingCriterionSet},
evaluation::AbstractEvaluationType = InplaceEvaluation(),
retraction_method::AbstractRetractionMethod = default_retraction_method(M),
vector_transport_method::AbstractVectorTransportMethod = default_vector_transport_method(M),
stepsize = WolfePowellLinesearch(M;
retraction_method = retraction_method,
vector_transport_method = vector_transport_method,
linesearch_stopsize = 1e-12),
kwargs...
)
opts = quasi_Newton(M,
loss,
gradF,
x0;
return_state = true,
evaluation,
retraction_method,
vector_transport_method,
stepsize,
stopping_criterion,
kwargs...)
opts = quasi_Newton(M, loss, gradF, x0; return_state = true, kwargs...)
# we unwrap DebugOptions here
minimizer = Manopt.get_solver_result(opts)
return (; minimizer = minimizer, minimum = loss(M, minimizer), options = opts)
Expand All @@ -200,18 +151,8 @@ function call_manopt_optimizer(M::ManifoldsBase.AbstractManifold,
loss,
gradF,
x0;
stopping_criterion::Union{Manopt.StoppingCriterion, Manopt.StoppingCriterionSet},
evaluation::AbstractEvaluationType = InplaceEvaluation(),
retraction_method::AbstractRetractionMethod = default_retraction_method(M),
vector_transport_method::AbstractVectorTransportMethod = default_vector_transport_method(M),
basis = Manopt.DefaultOrthonormalBasis(),
kwargs...)
opt = cma_es(M,
loss,
x0;
return_state = true,
stopping_criterion,
kwargs...)
opt = cma_es(M, loss, x0; return_state = true, kwargs...)
# we unwrap DebugOptions here
minimizer = Manopt.get_solver_result(opt)
return (; minimizer = minimizer, minimum = loss(M, minimizer), options = opt)
Expand All @@ -224,21 +165,8 @@ function call_manopt_optimizer(M::ManifoldsBase.AbstractManifold,
loss,
gradF,
x0;
stopping_criterion::Union{Manopt.StoppingCriterion, Manopt.StoppingCriterionSet},
evaluation::AbstractEvaluationType = InplaceEvaluation(),
retraction_method::AbstractRetractionMethod = default_retraction_method(M),
vector_transport_method::AbstractVectorTransportMethod = default_vector_transport_method(M),
kwargs...)
opt = convex_bundle_method!(M,
loss,
gradF,
x0;
return_state = true,
evaluation,
retraction_method,
vector_transport_method,
stopping_criterion,
kwargs...)
opt = convex_bundle_method(M, loss, gradF, x0; return_state = true, kwargs...)
# we unwrap DebugOptions here
minimizer = Manopt.get_solver_result(opt)
return (; minimizer = minimizer, minimum = loss(M, minimizer), options = opt)
Expand All @@ -252,21 +180,13 @@ function call_manopt_optimizer(M::ManifoldsBase.AbstractManifold,
gradF,
x0;
hessF = nothing,
stopping_criterion::Union{Manopt.StoppingCriterion, Manopt.StoppingCriterionSet},
evaluation::AbstractEvaluationType = InplaceEvaluation(),
retraction_method::AbstractRetractionMethod = default_retraction_method(M),
kwargs...)
opt = adaptive_regularization_with_cubics(M,
loss,
gradF,
hessF,
x0;
return_state = true,
evaluation,
retraction_method,
stopping_criterion,
kwargs...)
# we unwrap DebugOptions here

opt = if isnothing(hessF)
adaptive_regularization_with_cubics(M, loss, gradF, x0; return_state = true, kwargs...)
else
adaptive_regularization_with_cubics(M, loss, gradF, hessF, x0; return_state = true, kwargs...)
end
minimizer = Manopt.get_solver_result(opt)
return (; minimizer = minimizer, minimum = loss(M, minimizer), options = opt)
end
Expand All @@ -279,20 +199,12 @@ function call_manopt_optimizer(M::ManifoldsBase.AbstractManifold,
gradF,
x0;
hessF = nothing,
stopping_criterion::Union{Manopt.StoppingCriterion, Manopt.StoppingCriterionSet},
evaluation::AbstractEvaluationType = InplaceEvaluation(),
retraction_method::AbstractRetractionMethod = default_retraction_method(M),
kwargs...)
opt = trust_regions(M,
loss,
gradF,
hessF,
x0;
return_state = true,
evaluation,
retraction = retraction_method,
stopping_criterion,
kwargs...)
opt = if isnothing(hessF)
trust_regions(M, loss, gradF, x0; return_state = true, kwargs...)
else
trust_regions(M, loss, gradF, hessF, x0; return_state = true, kwargs...)
end
# we unwrap DebugOptions here
minimizer = Manopt.get_solver_result(opt)
return (; minimizer = minimizer, minimum = loss(M, minimizer), options = opt)
Expand All @@ -305,21 +217,8 @@ function call_manopt_optimizer(M::ManifoldsBase.AbstractManifold,
loss,
gradF,
x0;
stopping_criterion::Union{Manopt.StoppingCriterion, Manopt.StoppingCriterionSet},
evaluation::AbstractEvaluationType = InplaceEvaluation(),
retraction_method::AbstractRetractionMethod = default_retraction_method(M),
stepsize::Stepsize = DecreasingStepsize(; length = 2.0, shift = 2),
kwargs...)
opt = Frank_Wolfe_method(M,
loss,
gradF,
x0;
return_state = true,
evaluation,
retraction_method,
stopping_criterion,
stepsize,
kwargs...)
opt = Frank_Wolfe_method(M, loss, gradF, x0; return_state = true, kwargs...)
# we unwrap DebugOptions here
minimizer = Manopt.get_solver_result(opt)
return (; minimizer = minimizer, minimum = loss(M, minimizer), options = opt)
Expand All @@ -332,20 +231,22 @@ function SciMLBase.requiresgradient(opt::Union{
AdaptiveRegularizationCubicOptimizer, TrustRegionsOptimizer})
true
end
# TODO: WHY? they both still accept not passing it
function SciMLBase.requireshessian(opt::Union{
AdaptiveRegularizationCubicOptimizer, TrustRegionsOptimizer})
true
end

Comment on lines +234 to 239
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How is this function defined and what is it for?

The current definition here is not correct, both ARC and TR can perform their own (actually quite good) approximation of the hessian – similar to what QN does.
So they do not need a Hessian, but the exact one of course performs a bit better than the approximate one.

function build_loss(f::OptimizationFunction, prob, cb)
function (::AbstractManifold, θ)
return function (::AbstractManifold, θ)
x = f.f(θ, prob.p)
cb(x, θ)
__x = first(x)
return prob.sense === Optimization.MaxSense ? -__x : __x
end
end

#TODO: What does the “true” mean here?
function build_gradF(f::OptimizationFunction{true})
function g(M::AbstractManifold, G, θ)
f.grad(G, θ)
Expand All @@ -356,6 +257,7 @@ function build_gradF(f::OptimizationFunction{true})
f.grad(G, θ)
return riemannian_gradient(M, θ, G)
end
return g
end
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Where can I find more information about this? Especially what is the parameter {true}?


function build_hessF(f::OptimizationFunction{true})
Expand All @@ -373,6 +275,7 @@ function build_hessF(f::OptimizationFunction{true})
f.grad(G, θ)
return riemannian_Hessian(M, θ, G, H, X)
end
return h
end

function SciMLBase.__solve(cache::OptimizationCache{
Expand All @@ -395,8 +298,7 @@ function SciMLBase.__solve(cache::OptimizationCache{
LC,
UC,
S,
O <:
AbstractManoptOptimizer,
O <: AbstractManoptOptimizer,
D,
P,
C
Expand All @@ -418,6 +320,7 @@ function SciMLBase.__solve(cache::OptimizationCache{
u = θ,
p = cache.p,
objective = x[1])
#TODO: What is this callback for?
cb_call = cache.callback(opt_state, x...)
Comment on lines +323 to 324
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What is this callback, what is it used for and why is this here?

if !(cb_call isa Bool)
error("The callback should return a boolean `halt` for whether to stop the optimization process.")
Expand Down Expand Up @@ -448,10 +351,12 @@ function SciMLBase.__solve(cache::OptimizationCache{
stopping_criterion = Manopt.StopAfterIteration(500)
end

# TODO: With the new keyword warnings we can not just always pass down hessF!
opt_res = call_manopt_optimizer(manifold, cache.opt, _loss, gradF, cache.u0;
solver_kwarg..., stopping_criterion = stopping_criterion, hessF)
Comment on lines +354 to 356
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Since Manopt 0.5.22 this will in most cases warn if not error to pass a Hessian as a keyword argument to a solver that does not accept hessians. So this has to be reworked. How does Optimization.jl usually handle that some things are only used for some solvers?


asc = get_stopping_criterion(opt_res.options)
# TODO: Switch to `has_converged` once that was released.
opt_ret = Manopt.indicates_convergence(asc) ? ReturnCode.Success : ReturnCode.Failure

return SciMLBase.build_solution(cache,
Expand Down
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