[DEV] Add definition of final cost with JuMP.Model

Author: frapac

src/SDDPoptimize.jl

@@ -310,10 +310,13 @@ function initialize_value_functions( model::SPModel,
     # Build scenarios according to distribution laws:
     aleas = simulate_scenarios(model.noises, param.forwardPassNumber)
 
-    V[end] = model.finalCost
-
     # Add final costs to solverProblems:
-    build_terminal_cost!(model, solverProblems[end], V[end])
+    if isa(model.finalCost, PolyhedralFunction)
+        V[end] = model.finalCost
+        build_terminal_cost!(model, solverProblems[end], V[end])
+    elseif isa(model.finalCost, Function)
+        model.finalCost(model, solverProblems[end])
+    end
 
     stockTrajectories = forward_simulations(model,
                         param,
@@ -353,9 +356,16 @@ function hotstart_SDDP(model::SPModel, param::SDDPparameters, V::Vector{Polyhedr
 
     solverProblems = build_models(model, param)
 
-    for t in 1:model.stageNumber-1
+    for t in 1:model.stageNumber-2
         add_cuts_to_model!(model, t, solverProblems[t], V[t+1])
     end
+
+    # Take care of final cost:
+    if isa(model.finalCost, PolyhedralFunction)
+        add_cuts_to_model!(model, model.stageNumber-1, solverProblems[end], V[end])
+    else
+        model.finalCost(model, solverProblems[end])
+    end
     return solverProblems
 end
 

src/objects.jl

@@ -37,7 +37,7 @@ type LinearDynamicLinearCostSPmodel <: SPModel
     dynamics::Function
     noises::Vector{NoiseLaw}
 
-    finalCost::PolyhedralFunction
+    finalCost
 
     function LinearDynamicLinearCostSPmodel(nstage, ubounds, x0, cost, dynamic, aleas, Vfinal=nothing)
 
@@ -47,10 +47,10 @@ type LinearDynamicLinearCostSPmodel <: SPModel
 
         # First step: process terminal costs.
         # If not specified, default value is null function
-        if isa(Vfinal, Void)
-            Vf = PolyhedralFunction(zeros(1), zeros(1, dimStates), 1)
-        else
+        if isa(Vfinal, Function) || isa(Vfinal, PolyhedralFunction)
             Vf = Vfinal
+        else
+            Vf = PolyhedralFunction(zeros(1), zeros(1, dimStates), 1)
         end
 
         xbounds = []
@@ -79,17 +79,17 @@ type PiecewiseLinearCostSPmodel <: SPModel
     costFunctions::Vector{Function}
     dynamics::Function
     noises::Vector{NoiseLaw}
-    finalCost::PolyhedralFunction
+    finalCost
 
     function PiecewiseLinearCostSPmodel(nstage, ubounds, x0, costs, dynamic, aleas, Vfinal=nothing)
         dimStates = length(x0)
         dimControls = length(ubounds)
         dimNoises = length(aleas[1].support[:, 1])
 
-        if isa(Vfinal, Void)
-            Vf = PolyhedralFunction(zeros(1), zeros(1, dimStates), 1)
-        else
+        if isa(Vfinal, Function) || isa(Vfinal, PolyhedralFunction)
             Vf = Vfinal
+        else
+            Vf = PolyhedralFunction(zeros(1), zeros(1, dimStates), 1)
         end
 
         xbounds = []

test/runtests.jl

@@ -180,6 +180,18 @@ facts("SDDP algorithm: 1D case") do
         @fact isactive2 --> false
     end
 
+    # Test definition of final cost with a JuMP.Model:
+    context("Final cost") do
+        function fcost(model, m)
+            alpha = getVar(m, :alpha)
+            @addConstraint(m, alpha == 0.)
+        end
+        # Store final cost in model:
+        model.finalCost = fcost
+        V, pbs = solve_SDDP(model, params, 0)
+        V, pbs = solve_SDDP(model, params, 0, V)
+    end
+
     context("Piecewise linear cost") do
         # Test Piecewise linear costs:
         model = StochDynamicProgramming.PiecewiseLinearCostSPmodel(n_stages,