# Duals and constraint names for \`PowerModels\` and \`InfrastructureModels\`

**URL:** <https://discourse.julialang.org/t/duals-and-constraint-names-for-powermodels-and-infrastructuremodels/130311>\
**Category:** Optimization (Mathematical)\
**Tags:** powermodels\
**Created:** [June 29, 2025, 6:11pm UTC](https://discourse.julialang.org/t/duals-and-constraint-names-for-powermodels-and-infrastructuremodels/130311 "2025-06-29T18:11:14Z")\
**Posts on this page:** 6\
**Page:** 1

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**Author:** ![jacob-roth](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/jacob-roth/32/1862_2.png) [@jacob-roth](https://discourse.julialang.org/u/jacob-roth)\
**Post date:** [June 29, 2025, 6:11pm UTC](https://discourse.julialang.org/t/duals-and-constraint-names-for-powermodels-and-infrastructuremodels/130311/1 "2025-06-29T18:11:14Z")

</div>

I would like to (1) use `PowerModels` to construct a base DCOPF model and (2) make some modifications to the model (such as allowing load shed by adding new variables and modifying cosntraints). Beginning with an instance

```julia
file = "case30.m"
nd = PowerModels.parse_file(file)
pm = instantiate_model(network_data, DCPPowerModel, PowerModels.build_opf)
# ... modify `pm` to add new variables and constraints

```

is there a way to retrieve constraint names for each constraint and their duals/values? I see [`report_duals`](https://github.com/lanl-ansi/InfrastructureModels.jl/blob/74b8ca2f5131f2b0f25652fb6b557c0394fa6738/src/core/base.jl#L255) in `InfrastructureModels`, but I am not sure how to obtain a DCOPF model with constraint names and duals names beginning from my `nd` instance. Are the arguments I can pass to `instantiate_model` to do this?

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**Author:** ![odow](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/odow/32/28685_2.png) [@odow](https://discourse.julialang.org/u/odow)\
**Post date:** [June 29, 2025, 11:16pm UTC](https://discourse.julialang.org/t/duals-and-constraint-names-for-powermodels-and-infrastructuremodels/130311/2 "2025-06-29T23:16:06Z")

</div>

In most cases the constraints do not have names and they are not stored, so you cannot access their dual values. (There are some exceptions.)

See these issues:

> <https://github.com/lanl-ansi/PowerModels.jl/issues/876>
>
> Consider adding support for reporting duals of all constraints as per the discus…sion in https://github.com/lanl-ansi/PowerModels.jl/issues/409.

> <https://github.com/lanl-ansi/PowerModels.jl/issues/841>
>
> I want to retrieve the dual values associated with each constrain of the OPF (bu…s min/max voltages, line max loadings...). Is there a way to do that?
> 
> I am aware that this question was raised 2 years ago (https://discourse.julialang.org/t/powermodelsannex-jl-obtaining-duals/43535) but since I am a total beginner in Julia (I use the PandaPower interface), I was not able to follow the instruction given by @ccoffrin .
> 
> This might also be related to #661 . If so, sorry about the duplicate issue.
> 
> Thank you very much for your answer.

In most cases, if you want specific control over the subproblem and the duals, you should use PowerModels to parse the data and code your own implementation.

---

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**Author:** ![klamike](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/klamike/32/214776_2.png) [@klamike](https://discourse.julialang.org/u/klamike)\
**Post date:** [June 30, 2025, 1:21am UTC](https://discourse.julialang.org/t/duals-and-constraint-names-for-powermodels-and-infrastructuremodels/130311/3 "2025-06-30T01:21:15Z")

</div>

This is exactly what motivated us to build [PGLearn.jl](https://github.com/AI4OPT/PGLearn.jl), we similarly wanted 1) duals and 2) the ability to easily inspect/modify the formulations.

In [PGLearn.jl](https://github.com/AI4OPT/PGLearn.jl) each formulation is contained in one file – the one corresponding to PowerModels’ `DCPPowerModel` is [`DCOPF` in dcp.jl](https://github.com/AI4OPT/PGLearn.jl/blob/main/src/opf/dcp.jl). In that file you’ll find three functions – `build_opf` for building the model (which has all the constraints written plainly, so you can find/fork+modify them easily), `extract_primal` to get the primal solution, and `extract_dual` for the dual solution.

Here’s what your snippet would look like:

```julia
using PGLearn, PowerModels

# use PowerModels to parse the data like usual
file = "case30.m"
data = PowerModels.make_basic_network(PowerModels.parse_file(file))

# build the model
opf = PGLearn.build_opf(
  PGLearn.DCOPF,
  PGLearn.OPFData(data),
  nothing, # optimizer constructor, e.g. Ipopt.Optimizer
)

# solve the model
PGLearn.solve!(opf)

# extract the metadata/primal/dual solutions
results = PGLearn.extract_result(opf)

```

The documentation is currently quite sparse, focusing mostly on the math. But if it’s a good fit for your use case I’m happy to help you get started using it!

---

<div class="post-metadata">

**Author:** ![jacob-roth](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/jacob-roth/32/1862_2.png) [@jacob-roth](https://discourse.julialang.org/u/jacob-roth)\
**Post date:** [June 30, 2025, 3:28am UTC](https://discourse.julialang.org/t/duals-and-constraint-names-for-powermodels-and-infrastructuremodels/130311/4 "2025-06-30T03:28:46Z")

</div>

> [@klamike](#):
>
> PGLearn.jl

Oh great, thanks for the link! I think this should work for my purposes!

BTW, I’m using Julia 1.8 on a mac and had an error building `PGLearn`

```julia
julia> using PowerModels, PGLearn

[Info: Precompiling PGLearn [5d2523b5-5e96-4b1c-8178-da2b93e9175f]
ERROR: LoadError: UndefVarError: CHOLMOD not defined
Stacktrace:
  [1] getproperty(x::Module, f::Symbol)
    @ Base ./Base.jl:31
  [2] top-level scope
    @ ~/.julia/packages/PGLearn/m0m1y/src/opf/ptdf.jl:8
  [3] include(mod::Module, _path::String)
    @ Base ./Base.jl:419
  [4] include(x::String)
    @ PGLearn ~/.julia/packages/PGLearn/m0m1y/src/PGLearn.jl:1
  [5] top-level scope
    @ ~/.julia/packages/PGLearn/m0m1y/src/opf/opf.jl:304
  [6] include(mod::Module, _path::String)
    @ Base ./Base.jl:419
  [7] include(x::String)
    @ PGLearn ~/.julia/packages/PGLearn/m0m1y/src/PGLearn.jl:1
  [8] top-level scope
    @ ~/.julia/packages/PGLearn/m0m1y/src/PGLearn.jl:24
  [9] include
    @ ./Base.jl:419 [inlined]
 [10] include_package_for_output(pkg::Base.PkgId, input::String, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt64}}, source::Nothing)
    @ Base ./loading.jl:1554
 [11] top-level scope
    @ stdin:1
in expression starting at /Users/jakeroth/.julia/packages/PGLearn/m0m1y/src/opf/ptdf.jl:8
in expression starting at /Users/jakeroth/.julia/packages/PGLearn/m0m1y/src/opf/opf.jl:304
in expression starting at /Users/jakeroth/.julia/packages/PGLearn/m0m1y/src/PGLearn.jl:1
in expression starting at stdin:1
ERROR: Failed to precompile PGLearn [5d2523b5-5e96-4b1c-8178-da2b93e9175f] to /Users/jakeroth/.julia/compiled/v1.8/PGLearn/jl_8JGlEL.
Stacktrace:
 [1] error(s::String)
   @ Base ./error.jl:35
 [2] compilecache(pkg::Base.PkgId, path::String, internal_stderr::IO, internal_stdout::IO, keep_loaded_modules::Bool)
   @ Base ./loading.jl:1707
 [3] compilecache
   @ ./loading.jl:1651 [inlined]
 [4] _require(pkg::Base.PkgId)
   @ Base ./loading.jl:1337
 [5] _require_prelocked(uuidkey::Base.PkgId)
   @ Base ./loading.jl:1200
 [6] macro expansion
   @ ./loading.jl:1180 [inlined]
 [7] macro expansion
   @ ./lock.jl:223 [inlined]
 [8] require(into::Module, mod::Symbol)
   @ Base ./loading.jl:1144

```

with verision info:

```julia
julia> versioninfo()
Julia Version 1.8.5
Commit 17cfb8e65ea (2023-01-08 06:45 UTC)
Platform Info:
  OS: macOS (x86_64-apple-darwin21.4.0)
  CPU: 8 × Intel(R) Core(TM) i7-7820HQ CPU @ 2.90GHz
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-13.0.1 (ORCJIT, skylake)
  Threads: 1 on 8 virtual cores

```

I temporarily resolved the issue by changing the `LazyPTDF` struct field since `SparseArrays.CHOLMOD` wasn’t recognized for me:

```julia

struct LazyPTDF <: AbstractPTDF
    N::Int # number of buses
    E::Int # number of branches
    islack::Int # Index of slack bus

    A::SparseMatrixCSC{Float64,Int} # incidence matrix
    b::Vector{Float64} # branch susceptances
    BA::SparseMatrixCSC{Float64,Int} # B*A
    AtBA::SparseMatrixCSC{Float64,Int} # AᵀBA

    F::Factorization{Float64}#SparseArrays.CHOLMOD.Factor{Float64, Int64} # Factorization of AᵀBA
    #=
    julia> using LinearAlgebra;
    julia> using SparseArrays;
    julia> A = sprandn(10,10,0.2);
    julia> A=A'*A+I;
    julia> cholesky(A)
    SuiteSparse.CHOLMOD.Factor{Float64}
    type: LLt
    method: simplicial
    maxnnz: 28
    nnz: 28
    success: true
    julia> supertypes(typeof(F))
    (SuiteSparse.CHOLMOD.Factor{Float64}, Factorization{Float64}, Any)
    =#

    # TODO: cache
end

```

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<div class="post-metadata">

**Author:** ![klamike](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/klamike/32/214776_2.png) [@klamike](https://discourse.julialang.org/u/klamike)\
**Post date:** [June 30, 2025, 6:14pm UTC](https://discourse.julialang.org/t/duals-and-constraint-names-for-powermodels-and-infrastructuremodels/130311/5 "2025-06-30T18:14:08Z")

</div>

Nice catch! Fixing it in [Relax LazyPTDF factorization type by klamike · Pull Request #192 · AI4OPT/PGLearn.jl · GitHub](https://github.com/AI4OPT/PGLearn.jl/pull/192)

---

<div class="post-metadata">

**Author:** ![ccoffrin](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/ccoffrin/32/400_2.png) [@ccoffrin](https://discourse.julialang.org/u/ccoffrin)\
**Post date:** [July 5, 2025, 4:32pm UTC](https://discourse.julialang.org/t/duals-and-constraint-names-for-powermodels-and-infrastructuremodels/130311/6 "2025-07-05T16:32:44Z")

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@jacob-roth, I am a bit late to this thread but I wanted to remark, I think the better solution for your use case is to use one of the from scratch models in PowerModelsAnnex and then adapt to your needs, [PowerModelsAnnex.jl/src/model at master · lanl-ansi/PowerModelsAnnex.jl · GitHub](https://github.com/lanl-ansi/PowerModelsAnnex.jl/tree/master/src/model)

Then you can use JuMP’s functionality for extracting the dual information.