Comments (10)
odow
commented on July 17, 2024
1
No, it results in the same issue and it is the reproducer. I have identified the fix.
I don't know what you've done to your Julia instance, but you must be @evaling some code somewhere to get the world age issue.
from mathoptinterface.jl.
odow
commented on July 17, 2024
1
I mean, one could argue that they should be supported via falling back to the non-linear constraints
HiGHS does not support nonlinear constraints either. It is a mixed-integer linear solver, with additional support for continuous linear problems with a quadratic objective function.
from mathoptinterface.jl.
odow
commented on July 17, 2024
1
would it be useful to file an issue here about potentially bridging
No. We won't bridge constraints based on some numerical structure. Bridges must (with some limited exceptions) support any input data. In this case, you are asking if ScalarQuadraticFunction-in-EqualTo can be bridged to a collection of affine indicator constraints. This is only possible in your case where at least one element in each quadratic term is binary.
Instead of trying to write JuMP models using the high-level syntax like --> etc, if you know that you want to use HiGHS to solve the problem then I strongly encourage you to write out the MIP reformulation by hand.
from mathoptinterface.jl.
odow
commented on July 17, 2024
Reproducer:
julia> using JuMP, HiGHS
julia> model = Model(HiGHS.Optimizer)
A JuMP Model
Feasibility problem with:
Variables: 0
Model mode: AUTOMATIC
CachingOptimizer state: EMPTY_OPTIMIZER
Solver name: HiGHS
julia> @variable(model, x)
x
julia> @variable(model, z, Bin)
z
julia> @constraint(model, z --> {x * z == 0})
z --> {x*z = 0}
julia> optimize!(model)
Running HiGHS 1.7.0 (git hash: 50670fd4c): Copyright (c) 2024 HiGHS under MIT licence terms
ERROR: MethodError: no method matching bridge_constraint(::Type{…}, ::MathOptInterface.Bridges.LazyBridgeOptimizer{…}, ::MathOptInterface.VectorQuadraticFunction{…}, ::MathOptInterface.Indicator{…})
Closest candidates are:
bridge_constraint(::Type{MathOptInterface.Bridges.Constraint.VectorSlackBridge{T, F, S}}, ::Any, ::MathOptInterface.AbstractVectorFunction, ::S) where {T, F, S}
@ MathOptInterface ~/.julia/packages/MathOptInterface/2CULs/src/Bridges/Constraint/bridges/slack.jl:351
bridge_constraint(::Type{<:MathOptInterface.Bridges.Constraint.MultiSetMapBridge{T, S1, G}}, ::MathOptInterface.ModelLike, ::G, ::S1) where {T, S1, G}
@ MathOptInterface ~/.julia/packages/MathOptInterface/2CULs/src/Bridges/Constraint/set_map.jl:37
bridge_constraint(::Type{MathOptInterface.Bridges.Constraint.NumberConversionBridge{T, F1, S1, F2, S2}}, ::MathOptInterface.ModelLike, ::F1, ::S1) where {T, F1, S1, F2, S2}
@ MathOptInterface ~/.julia/packages/MathOptInterface/2CULs/src/Bridges/Constraint/bridges/number_conversion.jl:46
...
Stacktrace:
[1] add_bridged_constraint(b::MathOptInterface.Bridges.LazyBridgeOptimizer{…}, BridgeType::Type, f::MathOptInterface.VectorQuadraticFunction{…}, s::MathOptInterface.Indicator{…})
@ MathOptInterface.Bridges ~/.julia/packages/MathOptInterface/2CULs/src/Bridges/bridge_optimizer.jl:1786
[2] add_constraint(b::MathOptInterface.Bridges.LazyBridgeOptimizer{…}, f::MathOptInterface.VectorQuadraticFunction{…}, s::MathOptInterface.Indicator{…})
@ MathOptInterface.Bridges ~/.julia/packages/MathOptInterface/2CULs/src/Bridges/bridge_optimizer.jl:1916
[3] _copy_constraints(dest::MathOptInterface.Bridges.LazyBridgeOptimizer{…}, src::MathOptInterface.Utilities.UniversalFallback{…}, index_map::MathOptInterface.Utilities.IndexMap, index_map_FS::MathOptInterface.Utilities.DoubleDicts.IndexDoubleDictInner{…}, cis_src::Vector{…})
@ MathOptInterface.Utilities ~/.julia/packages/MathOptInterface/2CULs/src/Utilities/copy.jl:259
[4] _copy_constraints(dest::MathOptInterface.Bridges.LazyBridgeOptimizer{…}, src::MathOptInterface.Utilities.UniversalFallback{…}, index_map::MathOptInterface.Utilities.IndexMap, cis_src::Vector{…})
@ MathOptInterface.Utilities ~/.julia/packages/MathOptInterface/2CULs/src/Utilities/copy.jl:271
[5] pass_nonvariable_constraints_fallback(dest::MathOptInterface.Bridges.LazyBridgeOptimizer{…}, src::MathOptInterface.Utilities.UniversalFallback{…}, index_map::MathOptInterface.Utilities.IndexMap, constraint_types::Vector{…})
@ MathOptInterface.Utilities ~/.julia/packages/MathOptInterface/2CULs/src/Utilities/copy.jl:282
[6] pass_nonvariable_constraints(dest::MathOptInterface.Bridges.LazyBridgeOptimizer{…}, src::MathOptInterface.Utilities.UniversalFallback{…}, idxmap::MathOptInterface.Utilities.IndexMap, constraint_types::Vector{…})
@ MathOptInterface.Bridges ~/.julia/packages/MathOptInterface/2CULs/src/Bridges/bridge_optimizer.jl:445
[7] _pass_constraints(dest::MathOptInterface.Bridges.LazyBridgeOptimizer{…}, src::MathOptInterface.Utilities.UniversalFallback{…}, index_map::MathOptInterface.Utilities.IndexMap, variable_constraints_not_added::Vector{…})
@ MathOptInterface.Utilities ~/.julia/packages/MathOptInterface/2CULs/src/Utilities/copy.jl:330
[8] default_copy_to(dest::MathOptInterface.Bridges.LazyBridgeOptimizer{…}, src::MathOptInterface.Utilities.UniversalFallback{…})
@ MathOptInterface.Utilities ~/.julia/packages/MathOptInterface/2CULs/src/Utilities/copy.jl:505
[9] copy_to
@ ~/.julia/packages/MathOptInterface/2CULs/src/Bridges/bridge_optimizer.jl:455 [inlined]
[10] optimize!
@ ~/.julia/packages/MathOptInterface/2CULs/src/MathOptInterface.jl:84 [inlined]
[11] optimize!(m::MathOptInterface.Utilities.CachingOptimizer{…})
@ MathOptInterface.Utilities ~/.julia/packages/MathOptInterface/2CULs/src/Utilities/cachingoptimizer.jl:316
[12] optimize!(model::Model; ignore_optimize_hook::Bool, _differentiation_backend::MathOptInterface.Nonlinear.SparseReverseMode, kwargs::@Kwargs{})
@ JuMP ~/.julia/packages/JuMP/Gwn88/src/optimizer_interface.jl:457
[13] optimize!(model::Model)
@ JuMP ~/.julia/packages/JuMP/Gwn88/src/optimizer_interface.jl:409
[14] top-level scope
@ REPL[264]:1
Some type information was truncated. Use `show(err)` to see complete types.from mathoptinterface.jl.
LebedevRI
commented on July 17, 2024
That reproducer results in
Running HiGHS 1.6.0: Copyright (c) 2023 HiGHS under MIT licence terms
KERNEL EXCEPTION
MethodError: no method matching namemap(::Type{MathOptInterface.ActivationCondition})
The applicable method may be too new: running in world age 31466, while current world is 31501.
Closest candidates are:
namemap(::Type{MathOptInterface.ActivationCondition}) (method too new to be called from this world context.)
@ MathOptInterface Enums.jl:214
namemap(::Type{MathOptInterface.OptimizationSense}) (method too new to be called from this world context.)
@ MathOptInterface Enums.jl:214
namemap(::Type{LibGit2.Consts.GIT_TRACE_LEVEL})
@ LibGit2 Enums.jl:214
...
which seems like a different issue from the one i posted.
from mathoptinterface.jl.
odow
commented on July 17, 2024
Fix is #2507
Your underlying issue is that you cannot solve this constraint with HiGHS.jl. The next version of MOI will throw a better error instead of the uninformative MethodError.
[f=1:NUM_FRAMES, s=1:NUM_SWITCHES], SwitchDrivePower[f,s] --> { SwitchDriveDirection[f,s] == SwitchDrivePower[f,s] * SwitchDriveState[f,s] - 1 }from mathoptinterface.jl.
LebedevRI
commented on July 17, 2024
@odow thank you for taking a look!
Yup, i very much expected that it would be too good to work :)
The more faithful repro, which also does not work is
using JuMP, HiGHS
model = Model(HiGHS.Optimizer)
@variable(model, -1 <= x <= 1, Int)
@variable(model, -1 <= y <= 1, Int)
@variable(model, z, Bin)
# Variant using indicator constraints, works, but indicator constraints...
#@constraint(model, !z --> { y == 0 } )
#@constraint(model, z --> { y == x - 1 } )
@constraint(model, y == z * (x-1))
# You'd think it'd work, but:
# Constraints of type MathOptInterface.ScalarQuadraticFunction{Float64}-in-MathOptInterface.EqualTo{Float64} are not supported by the solver.
optimize!(model)
from mathoptinterface.jl.
odow
commented on July 17, 2024
HiGHS does not support quadratic constraints.
from mathoptinterface.jl.
LebedevRI
commented on July 17, 2024
Yes, i've understood that from the docs.
(I mean, one could argue that they should be supported via falling back to the non-linear constraints.)
I'm just showing the true snippet i was going for.
@odow thank you!
from mathoptinterface.jl.
LebedevRI
commented on July 17, 2024
@odow thank you.
Question:
would it be useful to file an issue here about potentially bridging
using JuMP, HiGHS
model = Model(HiGHS.Optimizer)
@variable(model, -1 <= x <= 1, Int)
@variable(model, -1 <= y <= 1, Int)
@variable(model, z, Bin)
@constraint(model, y == z * x)
=>
@constraint(model, !z --> { y == 0 } )
@constraint(model, z --> { y == x } )
?
I mean, one could argue that they should be supported via falling back to the non-linear constraints
HiGHS does not support nonlinear constraints either. It is a mixed-integer linear solver, with additional support for continuous linear problems with a quadratic objective function.
Again, i get that :) What i meant is, there seems to be (some?) support in JuMP for user-defined functions,
so perhaps the unsupported function types could be wrapped into such functions instead.
from mathoptinterface.jl.
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from mathoptinterface.jl.