MethodError: Cannot `convert` an object of type NonlinearExpr to an object of type Float64

[LebedevRI]

using JuMP, Ipopt

a = 3
b = 1

model = Model(Ipopt.Optimizer)

@variable(model, c[1:b])

@expression(model, d[i=1:b,j=1:a], j <= c[i])

@variable(model, e[i=1:b,j=1:a] == d[i,j])

print(model)

results in

MethodError: Cannot `convert` an object of type NonlinearExpr to an object of type Float64

Closest candidates are:
  convert(::Type{T}, ::Base.TwicePrecision) where T<:Number
   @ Base twiceprecision.jl:273
  convert(::Type{T}, ::AbstractChar) where T<:Number
   @ Base char.jl:185
  convert(::Type{T}, ::CartesianIndex{1}) where T<:Number
   @ Base multidimensional.jl:127
  ...

Stacktrace:
  [1] MathOptInterface.EqualTo{Float64}(value::NonlinearExpr)
    @ MathOptInterface ~/.julia/packages/MathOptInterface/wW7fs/src/sets.jl:223
  [2] _moi_constrain_variable(moi_backend::MathOptInterface.Utilities.CachingOptimizer{MathOptInterface.Bridges.LazyBridgeOptimizer{Ipopt.Optimizer}, MathOptInterface.Utilities.UniversalFallback{MathOptInterface.Utilities.Model{Float64}}}, index::MathOptInterface.VariableIndex, info::VariableInfo{Float64, Float64, NonlinearExpr, Float64}, #unused#::Type{Float64})
    @ JuMP ~/.julia/packages/JuMP/D44Aq/src/variables.jl:1768
  [3] _moi_add_variable(moi_backend::MathOptInterface.Utilities.CachingOptimizer{MathOptInterface.Bridges.LazyBridgeOptimizer{Ipopt.Optimizer}, MathOptInterface.Utilities.UniversalFallback{MathOptInterface.Utilities.Model{Float64}}}, model::Model, v::ScalarVariable{Float64, Float64, NonlinearExpr, Float64}, name::String)
    @ JuMP ~/.julia/packages/JuMP/D44Aq/src/variables.jl:1737
  [4] add_variable(model::Model, v::ScalarVariable{Float64, Float64, NonlinearExpr, Float64}, name::String)
    @ JuMP ~/.julia/packages/JuMP/D44Aq/src/variables.jl:1726
  [5] (::var"#85#86"{Model})(i::Int64, j::Int64)
    @ Main ~/.julia/packages/JuMP/D44Aq/src/Containers/macro.jl:301
  [6] #84
    @ ~/.julia/packages/JuMP/D44Aq/src/Containers/container.jl:85 [inlined]
  [7] iterate
    @ ./generator.jl:47 [inlined]
  [8] collect(itr::Base.Generator{JuMP.Containers.VectorizedProductIterator{Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}, JuMP.Containers.var"#84#85"{var"#85#86"{Model}}})
    @ Base ./array.jl:782
  [9] map(f::Function, A::JuMP.Containers.VectorizedProductIterator{Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}})
    @ Base ./abstractarray.jl:3291
 [10] container
    @ ~/.julia/packages/JuMP/D44Aq/src/Containers/container.jl:85 [inlined]
 [11] container
    @ ~/.julia/packages/JuMP/D44Aq/src/Containers/container.jl:71 [inlined]
 [12] container(f::Function, indices::JuMP.Containers.VectorizedProductIterator{Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}, #unused#::Type{JuMP.Containers.AutoContainerType}, names::Vector{Any})
    @ JuMP.Containers ~/.julia/packages/JuMP/D44Aq/src/Containers/container.jl:75
 [13] macro expansion
    @ ~/.julia/packages/JuMP/D44Aq/src/macros.jl:1213 [inlined]
 [14] top-level scope
    @ In[15]:12

It works if split-out the constraint:

using JuMP, Ipopt

a = 3
b = 1

model = Model(Ipopt.Optimizer)

@variable(model, c[1:b])

@expression(model, d[i=1:b,j=1:a], j <= c[i])

@variable(model, e[i=1:b,j=1:a])

@constraint(model, [i=1:b,j=1:a], e[i,j] == d[i,j])

print(model)

feasibility
Subject to
𝑒[1,1]−(1.0<=𝑐[1])=0
𝑒[1,2]−(2.0<=𝑐[1])=0
𝑒[1,3]−(3.0<=𝑐[1])=0

Related: https://github.com/scipopt/SCIP.jl/issues/282

[mlubin]

The error message could be improved, but this is invalid JuMP syntax. A variable bound (as specified in the @variable macro) must be a number and not an expression.

[LebedevRI]

Right. I've been hitting a number of issues, and it really wasn't obvious which ones are user errors. The error diagnostic could really use an improvement then. Thanks!

[blegat]

Yes, improving these errors makes a big difference for users but it's difficult for us to know what are the cases that don't have nice errors which is why open issues like this one are very helpful.

[LebedevRI]

Yes, improving these errors makes a big difference for users but it's difficult for us to know what are the cases that don't have nice errors which is why open issues like this one are very helpful.

Thanks! I've filed a few more cases i have previously hit. I think there was at least one more aside from these...

[LebedevRI]

@odow thank you!