How to use abs function in a objective function of JuMP + Julia

user_8387766 0
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I would like to solve a simple linear optimization problem with JuMP and Julia. This is my code:

using JuMP
using Mosek

model = Model(solver=MosekSolver())

@variable(model, 2.5 <= z1 <= 5.0)
@variable(model, -1.0 <= z2 <= 1.0)
@objective(model, Min, abs(z1+5.0) + abs(z2-3.0))

status = solve(model)
println("Objective value: ", getobjectivevalue(model)) 
println("z1:",getvalue(z1))
println("z2:",getvalue(z2))

However, I got this error message.

> ERROR: LoadError: MethodError: no method matching
> abs(::JuMP.GenericAffExpr{Float64,JuMP.Variable}) Closest candidates
> are:   abs(!Matched::Bool) at bool.jl:77   abs(!Matched::Float16) at
> float.jl:512   abs(!Matched::Float32) at float.jl:513

How can I use abs function in the JuMP code?

julia optimization
2 Answers

I did it on a diffrent way

AvgOperationtime = [1 2]#[2.0 2.0 2.0 3.3333333333333335 2.5 2.0 2.0 2.5 2.5 2.0 2.0]
    Operationsnumberremovecounter = [1 0;1 1]#[1.0 1.0 1.0 1.0 -0.0 1.0 1.0 1.0 -0.0 1.0 1.0; 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0; 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0; 1.0 1.0 1.0 -0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0; 1.0 1.0 1.0 -0.0 1.0 1.0 1.0 -0.0 1.0 1.0 1.0]
    Modelnumber = 2
    Operationsnumber = 2
    Basecaseworkload = 2
    y = 0.1
    Highestnumber = 999
    Solver = GLPK.Optimizer
    #Operationtime[1,1 X;0,9 2]
    m = Model(with_optimizer(Solver)); 
    @variable(m, Operationtime[1:Modelnumber,1:Operationsnumber]>=0);
    @variable(m, Absoluttime[1:Modelnumber,1:Operationsnumber]>=0);
    @variable(m, Absolutchoice[1:Modelnumber,1:Operationsnumber,1:2], Bin);
    @objective(m, Max, sum(Absoluttime[M,O]*Operationsnumberremovecounter[M,O] for M=1:Modelnumber,O=1:Operationsnumber))
    #How much Time can differ
    @constraint(m, BorderOperationtime1[M=1:Modelnumber,O=1:Operationsnumber], AvgOperationtime[O]*(1-y) <= Operationtime[M,O]);
    @constraint(m, BorderOperationtime2[M=1:Modelnumber,O=1:Operationsnumber], AvgOperationtime[O]*(1+y) >= Operationtime[M,O]);
    #Workload
    @constraint(m, Worklimit[O=1:Operationsnumber],  sum(Operationtime[M,O]*Operationsnumberremovecounter[M,O] for M=1:Modelnumber) == Basecaseworkload);
    #Absolut
    @constraint(m, Absolutchoice1[M=1:Modelnumber,O=1:Operationsnumber],  sum(Absolutchoice[M,O,X] for X=1:2) == 1);
    @constraint(m, Absoluttime1[M=1:Modelnumber,O=1:Operationsnumber], Absoluttime[M,O] <= Operationtime[M,O]-AvgOperationtime[O]+Absolutchoice[M,O,1]*Highestnumber);
    @constraint(m, Absoluttime2[M=1:Modelnumber,O=1:Operationsnumber], Absoluttime[M,O] <= AvgOperationtime[O]-Operationtime[M,O]+Absolutchoice[M,O,2]*Highestnumber);

    optimize!(m);

println("Termination status: ", JuMP.termination_status(m));
println("Primal status: ", JuMP.primal_status(m));
user_158251950
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