How to view which of your binary values are 1

Specific Domains Optimization (Mathematical)
1

I am very new to Julia. I wish to obtain the indices of my output that are non-zero

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2

Welcome to julia!, if you need only the indices, the following function can help you.

indices = findall(!iszero,-2:2) # [-2,-1,1,2]

there are some things going on with this

  1. findall acepts a function and a iterable container
  2. iszero(x) is a function that gives true if x is zero
  3. ! is a negation operator, and it works on functions too, when those functions give a boolean as output
  4. !iszero(x) then is a function that gives true if x is not zero, its not necessary to define a auxiliary function, is enough to write !iszero

!

3

Thanks for the response. Sorry I think I meant the key. In this image I have an example. If i input var1 I would like to retain [2.0,5.0,2.0,5.0] but not the others…
48%20PM%20(2)

4

var1 is a DenseAxisArray

5

mmm, i had never worked with DenseAxisArray, can you post the code necessary to generate that array?

6

You’re probably looking for something like:

julia> using JuMP

julia> using Gurobi

julia> model = Model(with_optimizer(Gurobi.Optimizer, OutputFlag=0));
Academic license - for non-commercial use only

julia> @variable(model, x[1:2, 1:3], Bin)
2Ă—3 Array{VariableRef,2}:
 x[1,1]  x[1,2]  x[1,3]
 x[2,1]  x[2,2]  x[2,3]

julia> @objective(model, Min, sum((-1)^(i + j) * x[i, j] for i in 1:2, j in 1:3))
x[1,1] - x[1,2] + x[1,3] - x[2,1] + x[2,2] - x[2,3]

julia> optimize!(model)
Academic license - for non-commercial use only

julia> x_val = value.(x)
2Ă—3 Array{Float64,2}:
 0.0  1.0  0.0
 1.0  0.0  1.0

julia> x[x_val .> 0.5]
3-element Array{VariableRef,1}:
 x[2,1]
 x[1,2]
 x[2,3]
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7 
using JuMP
using Gurobi
m=Model(with_optimizer(Gurobi.Optimizer,OutputFlag=0))
@variable(m,z[i_1=2:m1,i_2=i_1:m1,j_1=2:m2,j_2=j_1:m2],Bin)

Thank you! I tried this but I get an error because z is a DenseAxisArray and this is not supported.

8

So what about something like

using JuMP, Gurobi
m1 = 4
m2 = 4
m=Model(with_optimizer(Gurobi.Optimizer,OutputFlag=0))
@variable(m,z[i_1=2:m1,i_2=i_1:m1,j_1=2:m2,j_2=j_1:m2],Bin)
optimize!(model)
z_val = value.(z)

on_variables = [key for key in eachindex(z) if z_val[key] > 0.5]
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