Summing exponential expressions in NLP

Specific Domains Optimization (Mathematical)
2

I wasn’t able to reproduce your error because the code didn’t run (some missing values). If you have an example that reproduces the error, it would be good to post it.

In general you can sum exponential expressions in JuMP, at least this works:

using JuMP, Ipopt

####Me being creative with missing variables
numSteps = 50
minStep_int = 1
maxStep_int = 2
min_fl = 1
max_fl = 1
utz_fl = 0
ts_arr=ones(1,numSteps)
####
model = Model(Ipopt.Optimizer)

exp_var = @variable(model, exp, lower_bound = 0.0, upper_bound = 1000, start = rand(0:25))
multi_var = @variable(model, multi, lower_bound = 0.0, upper_bound = 1000, start = rand(0:25))
add_var = @variable(model, add, lower_bound = -1000, upper_bound = 1000, start = rand(-25:25))

scaledTs_var = @variable(model, tSteps[1:numSteps])

@NLconstraint(model, def[i = 1:numSteps], scaledTs_var[i] == multi_var * ts_arr[i]^exp_var + add_var)

@constraint(model, minVal, scaledTs_var[minStep_int] == min_fl)
@constraint(model, maxVal, scaledTs_var[maxStep_int] == max_fl)

# @objective(model, Min, (sum(scaledTs_var)- utz_fl)^2)  
# @objective(model, Min, (sum(map(i → multi_var * ts_arr[i]^exp_var + add_var,1:numSteps))- utz_fl)^2)

@NLobjective(model, Min, (sum(multi_var * ts_arr[i]^exp_var + add_var for i=1:numSteps)- utz_fl)^2)
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