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using JuMP
#Define model 
m = Model()
#Food available
S = ["brownies","ice cream","cola","cheese cake"]
#Non-negativity
@variable(m, x[S] >= 0)
#Minimum calories
@constraint(m, 400x["brownies"] + 200x["ice cream"] + 150x["cola"] + 500x["cheese cake"] >= 500)
#At least 6 grams of chocolate
@constraint(m, 3x["brownies"] + 2x["ice cream"] >= 6)
#At least 10 grams of sugar
@constraint(m, 2x["brownies"] + 2x["ice cream"] + 4x["cola"] + 4x["cheese cake"] >= 10)
#At least 8 grams of fat
@constraint(m, 2x["brownies"] + 4x["ice cream"] + 1x["cola"] + 5x["cheese cake"] >= 8)
#Minimize cost of consumption
@objective(m, Min, 0.5x["brownies"] + 0.2x["ice cream"] + 0.3x["cola"] + 0.8x["cheese cake"])
#Solve the optimization problem
solve(m)
#Determine consumption amounts
println("variable values: ", getvalue(x))
#Determine optimal cost of consumption
println("Objetive value: ", getobjectivevalue(m))


    

    




variable values: x: 1 dimensions:
[   brownies] = 0.0
[  ice cream] = 3.0000000000000004
[       cola] = 0.9999999999999998
[cheese cake] = 0.0

Objetive value: 0.9

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INFO: Nothing to be done

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INFO: Updating METADATA...
INFO: Computing changes...
INFO: Upgrading MathProgBase: v0.5.6 => v0.5.7

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