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Resolve R2 Allocation test #164

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Resolve R2 Allocation test #164

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1c94830
solve allocation tests
MaxenceGollier Dec 30, 2024
b5cbdc9
add `RegularizedExecutionStats`
MaxenceGollier Dec 31, 2024
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4 changes: 2 additions & 2 deletions src/R2_alg.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -132,7 +132,7 @@ For advanced usage, first define a solver "R2Solver" to preallocate the memory u
solver = R2Solver(reg_nlp)
solve!(solver, reg_nlp)

stats = GenericExecutionStats(reg_nlp.model)
stats = RegularizedExecutionStats(reg_nlp)
solver = R2Solver(reg_nlp)
solve!(solver, reg_nlp, stats)

Expand Down Expand Up @@ -292,7 +292,7 @@ function R2(reg_nlp::AbstractRegularizedNLPModel; kwargs...)
kwargs_dict = Dict(kwargs...)
max_iter = pop!(kwargs_dict, :max_iter, 10000)
solver = R2Solver(reg_nlp, max_iter = max_iter)
stats = GenericExecutionStats(reg_nlp.model)
stats = GenericExecutionStats(reg_nlp.model) # TODO: change this to `stats = RegularizedExecutionStats(reg_nlp)` when FHist etc. is ruled out.
cb = pop!(
kwargs_dict,
:callback,
Expand Down
20 changes: 20 additions & 0 deletions src/utils.jl
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@@ -1,3 +1,7 @@
export RegularizedExecutionStats

import SolverCore.GenericExecutionStats

# use Arpack to obtain largest eigenvalue in magnitude with a minimum of robustness
function LinearAlgebra.opnorm(B; kwargs...)
_, s, _ = tsvd(B)
Expand All @@ -20,3 +24,19 @@ ShiftedProximalOperators.iprox!(

LinearAlgebra.diag(op::AbstractDiagonalQuasiNewtonOperator) = copy(op.d)
LinearAlgebra.diag(op::SpectralGradient{T}) where {T} = zeros(T, op.nrow) .* op.d[1]

"""
GenericExecutionStats(reg_nlp :: AbstractRegularizedNLPModel{T, V})
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Construct a GenericExecutionStats object from an AbstractRegularizedNLPModel.
More specifically, construct a GenericExecutionStats on the NLPModel of reg_nlp and add three solver_specific entries namely :smooth_obj, :nonsmooth_obj and :xi.
This is useful for reducing the number of allocations when calling solve!(..., reg_nlp, stats) and should be used by default.
Warning: This should *not* be used when adding other solver_specific entries that do not have the current scalar type.
"""
function RegularizedExecutionStats(reg_nlp :: AbstractRegularizedNLPModel{T, V}) where{T, V}
stats = GenericExecutionStats(reg_nlp.model, solver_specific = Dict{Symbol, T}())
set_solver_specific!(stats, :smooth_obj, T(Inf))
set_solver_specific!(stats, :nonsmooth_obj, T(Inf))
set_solver_specific!(stats, :xi, T(Inf))
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Wouldn’t $\xi$ be in dual_feas?

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I don't see how $$\xi$$ is related with dual feasibility of the unconstrained problem but perhaps I am missing something ?

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By dual feasibility, we mean the gradient of the Lagrangian (for smooth problems). Here, the corresponding concept is $$\sqrt{\xi / \nu}$$.

return stats
end
2 changes: 1 addition & 1 deletion test/test_allocs.jl
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Expand Up @@ -41,7 +41,7 @@ end
for solver ∈ (:R2Solver,)
reg_nlp = RegularizedNLPModel(bpdn, h)
solver = eval(solver)(reg_nlp)
stats = GenericExecutionStats(bpdn, solver_specific = Dict{Symbol, Float64}())
stats = RegularizedExecutionStats(reg_nlp)
@test @wrappedallocs(solve!(solver, reg_nlp, stats)) == 0
end
end
Expand Down
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