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✅ All modified and coverable lines are covered by tests.
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  Coverage   99.49%   99.49%           
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  Lines         786      786           
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@tmigot Can you try this function to see if it allocates please.

function hess_op!(
  nlp::AbstractNLPModel,
  x::AbstractVector,
  Hv::AbstractVector,
  obj_weight::Real = one(eltype(x)),
)
  @lencheck nlp.meta.nvar x Hv
  prod = (res, v) -> hprod!(nlp, x, v, res; obj_weight = obj_weight)
  return LinearOperator{eltype(x)}(nlp.meta.nvar, nlp.meta.nvar, true, true, prod, prod, prod)
end

You allocate an array here: https://github.com/JuliaSmoothOptimizers/NLPModels.jl/pull/416/files#diff-3d235ab1458b43a45704d5735013ca8e8a94befce081da5f56722f0b95f71576R1141

@tmigot Can you try this function to see if it allocates please.

function hess_op!(
  nlp::AbstractNLPModel,
  x::AbstractVector,
  Hv::AbstractVector,
  obj_weight::Real = one(eltype(x)),
)
  @lencheck nlp.meta.nvar x Hv
  prod = (res, v) -> hprod!(nlp, x, v, res; obj_weight = obj_weight)
  return LinearOperator{eltype(x)}(nlp.meta.nvar, nlp.meta.nvar, true, true, prod, prod, prod)
end

  Problem name: BROWNDEN_manual
                   obj: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0.0   
              hess_op!: ████████████████████ 1136.0
           hess_coord!: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0.0   
                 grad!: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0.0   
       hess_structure!: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0.0   
                hprod!: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0.0   
         hess_op_prod!: ██⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 96.0  

You allocate an array here: https://github.com/JuliaSmoothOptimizers/NLPModels.jl/pull/416/files#diff-3d235ab1458b43a45704d5735013ca8e8a94befce081da5f56722f0b95f71576R1141

I am not sure to see which vector we allocate there

The creation of a linear operator structure just allocates maybe and it can't be avoided.

Hm, not sure why my link didn't work. I mean that hprod!() without the multipliers calls hprod!() with multipliers by allocating a vector of zero multipliers: https://github.com/JuliaSmoothOptimizers/NLPModels.jl/blob/main/src/nlp/api.jl#L1141. Could that be it?

The creation of a linear operator structure just allocates maybe and it can't be avoided.

That's probably true. And in fact I would expect hess_op!() to take an operator as argument and modify its prod, tprod, and ctprod.

Hm, not sure why my link didn't work. I mean that hprod!() without the multipliers calls hprod!() with multipliers by allocating a vector of zero multipliers: https://github.com/JuliaSmoothOptimizers/NLPModels.jl/blob/main/src/nlp/api.jl#L1141. Could that be it?

You are right, this allocates in general. But not here as we now redefine hprod in the test problems of NLPModelsTest https://github.com/JuliaSmoothOptimizers/NLPModelsTest.jl/blob/32ff072119733adc0c8637c2cc8380d108108939/src/nlp/problems/brownden.jl#L107

The creation of a linear operator structure just allocates maybe and it can't be avoided.

Okay, but still what is strange is that computing a product H * v also allocates (this is hess_op_prod!)

Ok. Did you confirm that https://github.com/JuliaSmoothOptimizers/NLPModelsTest.jl/blob/32ff072119733adc0c8637c2cc8380d108108939/src/nlp/problems/brownden.jl#L107 does not allocate?

Ok. Did you confirm that https://github.com/JuliaSmoothOptimizers/NLPModelsTest.jl/blob/32ff072119733adc0c8637c2cc8380d108108939/src/nlp/problems/brownden.jl#L107 does not allocate?

Yes, I updated JuliaSmoothOptimizers/NLPModelsTest.jl#60 (comment)

All the allocations are in LinearOperators.jl.
The main issue is this line: https://github.com/JuliaSmoothOptimizers/LinearOperators.jl/blob/main/src/abstract.jl#L75
We also always allocate two empty vectors: https://github.com/JuliaSmoothOptimizers/LinearOperators.jl/blob/main/src/abstract.jl#L74

using NLPModelsTest
nlp = eval(Meta.parse(NLPModelsTest.nlp_problems[1]))()
test_allocs_nlpmodels(nlp)

using Profile, PProf
Profile.Allocs.clear()
Profile.Allocs.@profile sample_rate=1 test_allocs_nlpmodels(nlp)
PProf.Allocs.pprof(from_c = false)

All the allocations are in LinearOperators.jl.
The main issue is this line: https://github.com/JuliaSmoothOptimizers/LinearOperators.jl/blob/main/src/abstract.jl#L75
We also always allocate two empty vectors: https://github.com/JuliaSmoothOptimizers/LinearOperators.jl/blob/main/src/abstract.jl#L74

I would say that those allocations are somewhat expected. This is the reason why I think hess_op!() needs to modify the operator in place, instead of creating a new one.