Add root_node_risk_measure kwarg to SDDP.train (#804)

src/algorithm.jl

@@ -116,6 +116,7 @@ struct Options{T}
     last_log_iteration::Ref{Int}
     # For threading
     lock::ReentrantLock
+    root_node_risk_measure::AbstractRiskMeasure
     # Internal function: users should never construct this themselves.
     function Options(
         model::PolicyGraph{T},
@@ -136,6 +137,7 @@ struct Options{T}
         duality_handler::AbstractDualityHandler = ContinuousConicDuality(),
         forward_pass_callback = x -> nothing,
         post_iteration_callback = result -> nothing,
+        root_node_risk_measure::AbstractRiskMeasure = Expectation(),
     ) where {T}
         return new{T}(
             initial_state,
@@ -160,6 +162,7 @@ struct Options{T}
             post_iteration_callback,
             Ref{Int}(0),  # last_log_iteration
             ReentrantLock(),
+            root_node_risk_measure,
         )
     end
 end
@@ -946,7 +949,10 @@ function iteration(model::PolicyGraph{T}, options::Options) where {T}
         )
     end
     @_timeit_threadsafe model.timer_output "calculate_bound" begin
-        bound = calculate_bound(model)
+        bound = calculate_bound(
+            model;
+            risk_measure = options.root_node_risk_measure,
+        )
     end
     lock(options.lock)
     try
@@ -1036,6 +1042,12 @@ Train the policy for `model`.
  - `risk_measure`: the risk measure to use at each node. Defaults to
    [`Expectation`](@ref).
 
+ -  `root_node_risk_measure::AbstractRiskMeasure`: the risk measure to use at
+    the root node when computing the `Bound` column. Note that the choice of
+    this option does not change the primal policy, and it applies only if the
+    transition from the root node to the first stage is stochastic. Defaults to
+    [`Expectation`](@ref).
+
  - `sampling_scheme`: a sampling scheme to use on the forward pass of the
     algorithm. Defaults to [`InSampleMonteCarlo`](@ref).
 
@@ -1086,6 +1098,7 @@ function train(
     run_numerical_stability_report::Bool = true,
     stopping_rules = AbstractStoppingRule[],
     risk_measure = SDDP.Expectation(),
+    root_node_risk_measure::AbstractRiskMeasure = Expectation(),
     sampling_scheme = SDDP.InSampleMonteCarlo(),
     cut_type = SDDP.SINGLE_CUT,
     cycle_discretization_delta::Float64 = 0.0,
@@ -1248,6 +1261,7 @@ function train(
         duality_handler,
         forward_pass_callback,
         post_iteration_callback,
+        root_node_risk_measure,
     )
     status = :not_solved
     try

src/plugins/parallel_schemes.jl

@@ -284,7 +284,10 @@ function master_loop(
             end
         end
         slave_update(model, result)
-        bound = calculate_bound(model)
+        bound = calculate_bound(
+            model;
+            risk_measure = options.root_node_risk_measure,
+        )
         push!(
             options.log,
             Log(

test/algorithm.jl

@@ -163,7 +163,7 @@ Unable to retrieve solution from node 1.
 
   Termination status : INFEASIBLE
   Primal status      : NO_SOLUTION
-  Dual status        : NO_SOLUTION.
+  Dual status        : INFEASIBILITY_CERTIFICATE.
 
 The current subproblem was written to `subproblem_1.mof.json`.
 
@@ -205,7 +205,7 @@ Unable to retrieve solution from node 1.
 
   Termination status : INFEASIBLE
   Primal status      : NO_SOLUTION
-  Dual status        : NO_SOLUTION.
+  Dual status        : INFEASIBILITY_CERTIFICATE.
 
 The current subproblem was written to `subproblem_1.mof.json`.
 
@@ -387,6 +387,33 @@ function test_numerical_difficulty_callback()
     return
 end
 
+function test_root_node_risk_measure()
+    model = SDDP.LinearPolicyGraph(;
+        stages = 3,
+        lower_bound = 0.0,
+        optimizer = HiGHS.Optimizer,
+    ) do sp, stage
+        @variable(sp, 0 <= x <= 100, SDDP.State, initial_value = 0)
+        @variable(sp, 0 <= u_p <= 200)
+        @variable(sp, u_o >= 0)
+        @variable(sp, w)
+        SDDP.parameterize(ω -> JuMP.fix(w, ω), sp, [100, 300])
+        @constraint(sp, x.out == x.in + u_p + u_o - w)
+        @stageobjective(sp, 100 * u_p + 300 * u_o + 50 * x.out)
+    end
+    SDDP.train(model; root_node_risk_measure = SDDP.WorstCase())
+    @test isapprox(model.most_recent_training_results.log[end].bound, 107500.0)
+    @test isapprox(
+        SDDP.calculate_bound(model; risk_measure = SDDP.WorstCase()),
+        107500.0,
+    )
+    @test isapprox(
+        SDDP.calculate_bound(model; risk_measure = SDDP.Expectation()),
+        85000.0,
+    )
+    return
+end
+
 end  # module
 
 TestAlgorithm.runtests()