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update Manopt and associated packages #906

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update Manopt and associated packages #906

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88e06da
update ManOpt and associated packages
oscardssmith Apr 29, 2025
daa0b2e
Update stepsize kwargs to use the default constructor
Vaibhavdixit02 Apr 29, 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 = "0.3.10, 0.4"
Manifolds = "0.9.18, 0.10"
ManifoldsBase = "0.15.10, 1"
Manopt = "0.5"
Optimization = "4"
Reexport = "1.2"
julia = "1.9"
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6 changes: 3 additions & 3 deletions lib/OptimizationManopt/src/OptimizationManopt.jl
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Expand Up @@ -67,7 +67,7 @@ function call_manopt_optimizer(
x0;
stopping_criterion::Union{Manopt.StoppingCriterion, Manopt.StoppingCriterionSet},
evaluation::AbstractEvaluationType = Manopt.AllocatingEvaluation(),
stepsize::Stepsize = ArmijoLinesearch(M),
stepsize::Stepsize = ArmijoLinesearchStepsize(M),
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This can for example stay as before, as I just wrote in the general comment ArmijoLiineSearch now introduces a factory (ok it is not a Stepsise type then, and when necessary one can produce_type on that, see the pattern in action at

https://github.com/JuliaManifolds/Manopt.jl/blob/013feadf95736639cdedb1bff2c4f1723f7dfe64/src/solvers/gradient_descent.jl#L208-L210

(note the new union there)

as well as

https://github.com/JuliaManifolds/Manopt.jl/blob/013feadf95736639cdedb1bff2c4f1723f7dfe64/src/solvers/gradient_descent.jl#L233C1-L233C45

which “turns” a factory into the concrete thing (here the stepwise) and is the identity if one already provides a stepwise.

kwargs...)
opts = gradient_descent(M,
loss,
Expand Down Expand Up @@ -174,7 +174,7 @@ function call_manopt_optimizer(M::Manopt.AbstractManifold,
stepsize = WolfePowellLinesearch(M;
retraction_method = retraction_method,
vector_transport_method = vector_transport_method,
linesearch_stopsize = 1e-12),
stop_when_stepsize_less = 1e-12),
kwargs...
)
opts = quasi_Newton(M,
Expand Down Expand Up @@ -308,7 +308,7 @@ function call_manopt_optimizer(M::ManifoldsBase.AbstractManifold,
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),
stepsize::Stepsize = DecreasingLength(; length = 2.0, shift = 2),
kwargs...)
opt = Frank_Wolfe_method(M,
loss,
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2 changes: 1 addition & 1 deletion lib/OptimizationManopt/test/runtests.jl
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Expand Up @@ -33,7 +33,7 @@ end
x0 = zeros(2)
p = [1.0, 100.0]

stepsize = Manopt.ArmijoLinesearch(R2)
stepsize = Manopt.ArmijoLinesearch()
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Ah note that this for example is still “just” a factory when written this way, so maybe an OptimizationProblem should use our internal function _produce_type or maybe better only rely on the concrete step size types and not the factories.

I hope this does not sound too chaotic, as the one having designed it, to me the scheme is relatively clear, and I hope I am able to explain it well enough.

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yeah. I think what we probably want to do is hook into the init and resolve the factory at that time? @ChrisRackauckas what do you think?

opt = OptimizationManopt.GradientDescentOptimizer()

optprob_forwarddiff = OptimizationFunction(rosenbrock, Optimization.AutoEnzyme())
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