How to solve the deterministic equivalent

Sometimes, it can be helpful to solve the deterministic equivalent of a problem in order to obtain an exact solution to the problem. To obtain a JuMP model that represents the deterministic equivalent, use SDDP.deterministic_equivalent. The returned model is just a normal JuMP model. Use JuMP to optimize it and query the solution.

julia> using SDDP, HiGHSjulia> model = SDDP.LinearPolicyGraph(                   stages = 2,                   lower_bound = 0.0,                   optimizer = HiGHS.Optimizer,               ) do subproblem, t           @variable(subproblem, x, SDDP.State, initial_value = 1)           @variable(subproblem, y)           @constraint(subproblem, balance, x.in == x.out + y)           SDDP.parameterize(subproblem, [1.1, 2.2]) do ω               @stageobjective(subproblem, ω * x.out)               fix(y, ω)           end       endA policy graph with 2 nodes. Node indices: 1, 2julia> det_equiv = SDDP.deterministic_equivalent(model, HiGHS.Optimizer)A JuMP Model├ solver: HiGHS├ objective_sense: MIN_SENSE│ └ objective_function_type: AffExpr├ num_variables: 24├ num_constraints: 28│ ├ AffExpr in MOI.EqualTo{Float64}: 10│ ├ VariableRef in MOI.EqualTo{Float64}: 8│ ├ VariableRef in MOI.GreaterThan{Float64}: 6│ └ VariableRef in MOI.LessThan{Float64}: 4└ Names registered in the model: nonejulia> set_silent(det_equiv)julia> optimize!(det_equiv)julia> objective_value(det_equiv)-5.472500000000001