Developer-focused chat about JuMP, M̶a̶t̶h̶P̶r̶o̶g̶B̶a̶s̶e̶, MathOptInterface, solver interfaces, and JuMP extensions. Towards JuMP 1.0! Basic usage questions will be answered more quickly at https://discourse.julialang.org/c/domain/opt.
I'm getting
MathOptInterface.UnsupportedConstraint{MathOptInterface.VectorAffineFunction{Float64},MathOptInterface.LogDetConeSquare}: `MathOptInterface.VectorAffineFunction{Float64}`-in-`MathOptInterface.LogDetConeSquare` constraint is not supported by the model.`(where my model is model = MOIB.full_bridge_optimizer(MOIU.CachingOptimizer(MOIU.UniversalFallback(MOIU.Model{T}()),
SCS.Optimizer()), T)). Does that mean I need to formulate the constraint differently? What way would be accepted?
UnsupportedAttribute is clearly a user error. If you called function X that called function Y that called function Z that asked for an attribute, then it's probably the responsibility of one of those functions to catch the error.
[slack] <mtanneau> That errors eventually means that the attribute you called is not supported by the underlying solver.
The first step should be to look at the MOI documentation to rule out typos, signature, etc.
The second step should be to look at that solver’s docs, and see whether it indeed supports said attribute. For instance, does it support setting the number of threads? If yes, then it should probably be exposed through MOI, and if it isn’t, do open an issue with that solver’s wrapper. If no, then there’s no way MOI can expose a functionality that does not exist :man-shrugging:
https://julianlsolvers.github.io/Optim.jl/stable/#algo/ipnewton/
Re gams: “New output format: JuMP scalar model.” I wonder what they mean by “scalar model”. I could only find this https://www.gams.com/blog/2020/06/new-and-improved-gams-links-for-pyomo-and-jump/
I believe scalar model means "not using sets" for constraints. So, if you had the same kind of constraint for every node in a graph, say, you would use a for loop in GAMS or JuMP. But the GAMS-generated JuMP model would contain many individual constraints.
MOI.RelativeEntropyCone(n) where n was the length of my vectors, instead of 2n+1 which is the dimension of the cone. The tricky thing is that it solved the problem without emitting errors and even found almost the right optimal value of the variables, but the objective value was 0 instead of what it should be. I was wondering if there is a way to add a dimension check so that it would error if the wrong dimension is supplied.
We now have a SCIP_jll package (thanks @matbesancon), and are working on a SCIP-Interfaces/SCIP.jl#177 to integrate with SCIP.jl.
It works fine on Linux and OS X (via Travis), but fails on Windows (via AppVeyor). I have the feeling that there is an issue with shared library generated by BinaryBuilder, because the same Julia version work when we download the binaries from the SCIP website.
Do you have any suggestions on debugging this, preferably not using a Window machine?
I'm working on https://github.com/Wikunia/ConstraintSolver.jl but it is currently a hobby project and you're probably looking for useable solvers 😂
trying to revive my NLOptControl package and there is an issue creating variables, I have continuous variables, but somehow my call to the @variable macro defines my variables as discrete and I get this error
ERROR: LoadError: Solver does not support discrete variables
Stacktrace:
[1] error(::String) at ./error.jl:33
[2] _buildInternalModel_nlp(::JuMP.Model, ::JuMP.ProblemTraits) at /home/febbo/.julia/packages/JuMP/I7whV/src/nlp.jl:1249because I am using IPOPT. I checked out the new documentation, to see if I can make sure that the variables are continuous http://jump.dev/JuMP.jl/v0.21.3/variables/ but did not notice anything. Is there a way to ensure that the variables are continuous when they are defined?