xhub/emp.jl

Extended Mathematical Programming in Julia

EMP

This package is not for public use as it needs to be updated to the latest version of JuMP/MOI.

This package enables the modeling of Extending Mathematical Programming (EMP) concepts within the JuliaOpt ecosystem. Broadly speaking, EMP enables the modeling of optimization problems with a structure that do not fit the classical minimization problem. For instance, the following problems can be modeled with EMP

• Variational Inequalities (VI and AVI) or Complementarity Problems (LCP and NCP)
• (Generalized) Nash Equilibrium Problem (NEP and GNEP) and MOPEC (Multiple Optimization Problems with Equilibrium Constraints)
• Optimization Problems with Optimal Value Function (OVF) in the problem data. Examples of OVF include coherent risk measures (CVaR), convex regularizers/loss function (Huber, Hinge, l1, ...)

A problem with EMP can be solved by using model transformation to obtain a form amenable to computations by existing solvers. Currently, using the ReSHOP library is the only option to solve an optimization problem with EMP data structure.

Design

This package is designed as a thin layer over an existing modeling framework in JuliaOpt. Right now, only JuMP is supported. The idea is to use this framework to store variables and equations. The additional information to capture the EMP concept is stored in an EMP master object.

This package relies on ReSHOP.jl to export the model and the EMP information to the ReSHOP library. The latter is going to perform the necessary model transformations.

Note: This package is developed independently of the GAMS corporation

emp.jl's Issues

Review

General comments:

These are mainly observations looking at the examples.

• The new version of JuMP will make this whole package much easier
  You will be able to have something like the following (just making syntax up, not an actual model)

emp = EMPModel(agent_models = n)

@variable(emp, x[i=1:n] >= 0)
@constraint(emp, [i=1:n], x[i] in AgentModel(i))
@constraint(emp, sum(x) >= 1)

for (i, agent) in enumerate(agents(emp))
    @agentobjective(agent, Min, x[i])
end

@variable(emp, y)
@constraint(emp, (x[1], y) in VariationalPair())

• I'm not sure that there is need to support Convex going forward. The new JuMP/MOI is converging towards DCP. This removes a whole heap of the conditional code loading.
• Have you seen https://github.com/StructJuMP/StructJuMP.jl? This also has multiple models with different scoping.
• Have you seen https://github.com/jalving/Plasmo.jl?
• The is the approach I would favour is almost identical to Plasmo. There are scoping issues that need to overcome, but these are just Julia problems:

agent_models = [JuMP.Model() for i in 1:2]
for agent in 1:2
    # x belongs to agent_model[i]
    @variable(agent_model[i], x >= 0)
    @objective(agent_model[i], Min, x)
end
# x is now x[2]
emp = EMPModel(agent_models)
# but we can get x[1] via agent_models[1][:x]
@empconstraint(emp, sum(m[:x] for m in agent_models) >= 1)
solve(emp)

or

agent_models = [JuMP.Model() for i in 1:2]
x = Dict{Int, JuMP.Variable}()
for i in 1:2
    x[i] = @variable(agent_model[i], lower_bound = 0)
    @objective(agent_model[i], Min, x[i])
end
# now we have x[1] and x[2]
emp = EMPModel(agent_models, solver=JAMSDSolver())
@empconstraint(emp, sum(x[i] for i in 1:2) >= 1)
solve(emp)

It's then up to EMPModel to combine the models on the back end.

Pedantic comments:

A lot of the code design stems from the limitations of the current form of JuMP and MPB. Hence the need for the MOI redesign. The new JuMP NL implementation splits out the linear constraints from the non-linear constraints which should help resolve

EMP.jl/src/EMP.jl

Line 374 in 36d877d

# TODO(xhub) fix this mess ...

• You don't need to export getsolution etc.

  EMP.jl/src/EMP.jl

  Line 16 in 36d877d

  export @variableMP, @objectiveMP, @NLobjectiveMP, @constraintMP, @NLconstraintMP, vipair, @NLvipair, solveEMP, _solveEMP, MathPrgm, addvar!, addequ!, addovf!, getsolution, status, get_solve_result, get_solve_result_num, get_model_result, get_model_result_num, get_solve_message, get_solve_exitcode, solve

  
  Instead of making new methods, you should import them from MPB and extend them.
• In SDDP.jl, I make the user go using SDDP, JuMP. You could also look into the https://github.com/simonster/Reexport.jl
• proposal. Change all the @constraintMP et at. macros to @empconstraint, @empnlconstraint.
• For
  

  EMP.jl/src/EMP.jl

  Line 171 in 36d877d

  code = :( v = @eval @variable $(mp).emp.model_ds )

  
  you probably want something like
  https://github.com/odow/SDDP.jl/blob/be885b296741b3f3ec89659aea796492290445fc/src/states.jl#L116

I can not install it

I tried installing EMP.jl by cloning the repository and activating it. It fails when installing ReSHOP (that is also written by you) because the file libreshop.tar.xz is 0 bytes long. Then, tar fails to cope with it. Are EMP.jl and ReSHOP.jl still active? Do you have any plans to update them? If not, any suggestions of another Julia package with similar capabilities?