Method Error with no method matching zero(::Type{SDDP.State{VariableRef}})

Specific Domains Optimization (Mathematical)
2

Hi @Millie, welcome to the forum.

The error message is very cryptic, but the issue is in this line:

@stageobjective(subproblem, firm_value * exit_decision - total_invest - downturn)

You have used total_invest without specifying whether you meant .in or .out.

Here’s how I would write your model. I have started to use x_ as a prefix for state variables to make it easier to spot when we should use .in and .out.

using SDDP, Gurobi
model = SDDP.LinearPolicyGraph(;
    stages = 5,
    sense = :Max,
    upper_bound = 500.0,
    optimizer = Gurobi.Optimizer,
) do subproblem, t
    @variables(subproblem, begin
        x_invest_decision, Bin, SDDP.State, (initial_value = 1)
        0 <= x_total_invest <= 100, SDDP.State, (initial_value = 10)
        u_exit_decision, Bin
    end)
    @constraints(subproblem, begin
        x_invest_decision.out + u_exit_decision == x_invest_decision.in
        x_total_invest.out == x_total_invest.in + 10*x_invest_decision.out
    end)
    SDDP.add_objective_state(
        subproblem;
        initial_value = 100.0,
        lipschitz = 1.0
    ) do p_firm_value, ω
        return p_firm_value + ω.fv_noise
    end
    Ω = [
        (fv_noise = f, downturn = w)
        for f in [10, 20, 30, 40]
        for w in [0.0,0.0,0.0,-50.0, -100.0]
    ]
    SDDP.parameterize(subproblem, Ω) do ω
        p_firm_value = SDDP.objective_state(subproblem)
        @stageobjective(
            subproblem, 
            p_firm_value * u_exit_decision - x_total_invest.out - ω.downturn,
        )
        return
    end
end
SDDP.train(model)
simulations = SDDP.simulate(model, 1)

I don’t think your logic around the exit_decision was quite correct though, so I made a small change. I assume you still have some changes coming around the total_invest state.

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