Merge pull request #41 from leclere/cleaning-examples

[FIX] a few hotfixes

Author: leclere

examples/dam.jl

@@ -59,7 +59,7 @@ function cost_t(t, x, u, w)
 end
 
 
-"""Solve the problem with a solver, supposing the aleas are known
+"""Solve the problem assuming the aleas are known
 in advance."""
 function solve_determinist_problem()
     println(alea_year)

examples/damsvalley.jl

@@ -59,7 +59,7 @@ function cost_t(t, x, u, w)
 end
 
 
-"""Solve the problem with a solver, supposing the aleas are known
+"""Solve the problem assuming the aleas are known
 in advance."""
 function solve_determinist_problem()
     m = Model(solver=SOLVER)

examples/onedam.jl

@@ -1,16 +1,27 @@
-srand(2713)
-push!(LOAD_PATH, "../src")
-# include("../src/objects.jl")
-# include("../src/SDPoptimize.jl")
+#  Copyright 2015, Vincent Leclere, Francois Pacaud and Henri Gerard
+#  This Source Code Form is subject to the terms of the Mozilla Public
+#  License, v. 2.0. If a copy of the MPL was not distributed with this
+#  file, You can obtain one at http://mozilla.org/MPL/2.0/.
+#############################################################################
+# Compare different ways of solving a stock problem
+#############################################################################
 
+push!(LOAD_PATH, "../src")
 using StochDynamicProgramming, JuMP, Clp, Distributions
 
+######## Optimization parameters  ########
+# choose the LP solver used. 
 const SOLVER = ClpSolver()
 # const SOLVER = CplexSolver(CPX_PARAM_SIMDISPLAY=0)
+# const SOLVER = GurobiSolver()
 
+# Stopping test parameters
 const EPSILON = .05
 const MAX_ITER = 20
 
+
+######## Stochastic problem parameters  ########
+
 # Define number of stages and scenarios:
 const N_STAGES = 3
 const N_SCENARIOS = 10
@@ -19,7 +30,7 @@ const N_SCENARIOS = 10
 const TF = N_STAGES-1
 
 # Randomnly generate a cost scenario fixed for the whole problem:
-const COST = [-12, -200, -67]
+const COST = -rand(N_STAGES)
 
 # Define bounds for states and controls:
 const VOLUME_MAX = 50
@@ -81,7 +92,7 @@ function constraints(t, x1, u, w)
 end
 
 
-"""Solve the problem with a solver, supposing the aleas are known
+"""Solve the optimization problem assuming the aleas are known
 in advance."""
 function solve_determinist_problem()
     m = Model(solver=SOLVER)

src/noises.jl

@@ -169,7 +169,7 @@ Returns :
 - scenarios Array(Float64,n,T)
     an Array of scenarios, scenarios[i,:] being the ith noise scenario
 """
-function generate_scenarios(law::Vector{NoiseLaw}, n::Int64)
+function generate_scenarios(laws::Vector{NoiseLaw}, n::Int64)
     if n <= 0
         error("negative number of simulations")
     end
@@ -178,7 +178,7 @@ function generate_scenarios(law::Vector{NoiseLaw}, n::Int64)
     for i = 1:n#TODO can be parallelized
         scenario = []
         for t=1:Tf
-            new_val = law[t].support[:, rand(Categorical(law[t].proba))]
+            new_val = laws[t].support[:, rand(Categorical(law[t].proba))]
             push!(scenario, new_val)
         end
         scenarios[i,:]=scenario

src/objects.jl

@@ -142,16 +142,16 @@ type StochDynProgModel <: SPModel
 end
 
 type SDDPparameters
-    # Solver to solve
+    # Solver used to solve LP
     solver
-    # number of simulated scenario in the forward pass
+    # number of scenarios in the forward pass
     forwardPassNumber::Int64
     # Admissible gap between the estimation of the upper-bound
     sensibility::Float64
     # Maximum iterations of the SDDP algorithms:
     maxItNumber::Int64
 
-    function SDDPparameters(solver, passnumber, sensibility=0.01, max_iterations=20)
+    function SDDPparameters(solver, passnumber=10, sensibility=0., max_iterations=20)
         return new(solver, passnumber, sensibility, max_iterations)
     end
 end