Merge previous branch

Author: frapac

src/SDDPoptimize.jl

@@ -370,7 +370,12 @@ function build_model(model, param, t,verbosity::Int64=0)
 
     # Define objective function (could be linear or piecewise linear)
     if isa(model.costFunctions, Function)
+        try
             @objective(m, Min, model.costFunctions(t, x, u, w) + alpha)
+        catch
+            @objective(m, Min, model.costFunctions(m, t, x, u, w) + alpha)
+        end
+
 
     elseif isa(model.costFunctions, Vector{Function})
         @variable(m, cost)

src/noises.jl

@@ -22,6 +22,9 @@ mutable struct NoiseLaw
     function NoiseLaw(dimNoises, supportSize, support, proba)
         dimNoises = convert(Int64,dimNoises)
         supportSize = convert(Int64,supportSize)
+        if ndims(support)==1
+            support = reshape(support,length(support),1)
+        end
 
         support, proba = reshaping_noise(support, proba)
 

src/oneStepOneAleaProblem.jl

@@ -66,8 +66,11 @@ function solve_one_step_one_alea(model,
 
     #update objective
     if isa(model.costFunctions, Function)
-        @objective(m, Min, model.costFunctions(t, x, u, xi) + alpha)
-
+        try
+            @objective(m, Min, model.costFunctions(t, x, u, xi) + alpha)
+        catch
+            @objective(m, Min, model.costFunctions(m, t, x, u, xi) + alpha)
+        end
     elseif isa(model.costFunctions, Vector{Function})
         cost = getindex(m, :cost)
         for i in 1:length(model.costFunctions)
@@ -92,7 +95,7 @@ function solve_one_step_one_alea(model,
         solved = relaxation ? solve_relaxed!(m, param,verbosity) : solve_mip!(m, param,verbosity)
     else
         status = (verbosity>3) ? solve(m, suppress_warnings=false) : solve(m, suppress_warnings=false)
-        solved = (status == :Optimal)
+        solved = (status == :Optimal) || (status == :Suboptimal)
     end
 
     # get time taken by the solver:
@@ -114,6 +117,8 @@ function solve_one_step_one_alea(model,
         println(status)
         error("Foo")
         # If no solution is found, then return nothing
+        #= println(m) =#
+        #= error("Fail to solve") =#
         result = NLDSSolution()
     end