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Merge pull request #85 from leclere/dev-release-v0.1.2
Dev release v0.1.2
2 parents 77d7b06 + b9aa095 commit 5c56bae

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‎NEWS.md‎

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# StochDynamicProgramming.jl release notes
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## Version v0.1.2
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* Update to JuMP 0.13
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* Add a proper way to define final cost with JuMP
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* return control in extensive formulation
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## Version v0.1.1
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* Add cuts pruning
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* Update documentation
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* Improve coverage of unit-tests
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‎REQUIRE‎

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julia 0.4
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MathProgBase
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Clp
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JuMP 0.13
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Distributions
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JuMP
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FactCheck
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ProgressMeter
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Interpolations
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Iterators

‎doc/conf.py‎

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import sys
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import os
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import sphinx_rtd_theme
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import juliadoc
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# If extensions (or modules to document with autodoc) are in another directory,
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# add these directories to sys.path here. If the directory is relative to the
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# ones.
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extensions = [
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'sphinx.ext.coverage',
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'sphinx.ext.mathjax', 'juliadoc.julia', 'juliadoc.jlhelp'
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'sphinx.ext.mathjax',
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]
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# Add any paths that contain templates here, relative to this directory.

‎examples/benchmark.jl‎

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#############################################################################
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srand(2713)
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push!(LOAD_PATH, "../src")
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using StochDynamicProgramming, JuMP
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using Clp
@@ -51,23 +50,23 @@ function solve_anticipative_problem(model, scenario)
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m = Model(solver=SOLVER)
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@defVar(m, model.xlim[1][1] <= x1[1:(N_STAGES)] <= model.xlim[1][2])
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@defVar(m, model.xlim[2][1] <= x2[1:(N_STAGES)] <= model.xlim[2][2])
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@defVar(m, model.ulim[1][1] <= u1[1:N_STAGES-1] <= model.ulim[1][2])
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@defVar(m, model.ulim[2][1] <= u2[1:N_STAGES-1] <= model.ulim[2][2])
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@variable(m, model.xlim[1][1] <= x1[1:(N_STAGES)] <= model.xlim[1][2])
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@variable(m, model.xlim[2][1] <= x2[1:(N_STAGES)] <= model.xlim[2][2])
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@variable(m, model.ulim[1][1] <= u1[1:N_STAGES-1] <= model.ulim[1][2])
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@variable(m, model.ulim[2][1] <= u2[1:N_STAGES-1] <= model.ulim[2][2])
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@setObjective(m, Min, sum{COST[i]*(u1[i] + u2[i]), i = 1:N_STAGES-1})
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@objective(m, Min, sum{COST[i]*(u1[i] + u2[i]), i = 1:N_STAGES-1})
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for i in 1:N_STAGES-1
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@addConstraint(m, x1[i+1] - x1[i] + u1[i] - scenario[i] == 0)
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@addConstraint(m, x2[i+1] - x2[i] + u2[i] - u1[i] == 0)
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@constraint(m, x1[i+1] - x1[i] + u1[i] - scenario[i] == 0)
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@constraint(m, x2[i+1] - x2[i] + u2[i] - u1[i] == 0)
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end
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@addConstraint(m, x1[1] == model.initialState[1])
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@addConstraint(m, x2[1] == model.initialState[2])
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@constraint(m, x1[1] == model.initialState[1])
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@constraint(m, x2[1] == model.initialState[2])
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status = solve(m)
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return getObjectiveValue(m)
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return getobjectivevalue(m)
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end
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‎examples/dam.jl‎

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# Source: Adrien Cassegrain
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#############################################################################
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push!(LOAD_PATH, "../src")
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using StochDynamicProgramming, JuMP, Clp
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println(alea_year)
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m = Model(solver=SOLVER)
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@defVar(m, 0 <= x[1:N_STAGES] <= 100)
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@defVar(m, 0. <= u[1:N_STAGES-1] <= 7)
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@defVar(m, 0. <= s[1:N_STAGES-1] <= 7)
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@variable(m, 0 <= x[1:N_STAGES] <= 100)
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@variable(m, 0. <= u[1:N_STAGES-1] <= 7)
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@variable(m, 0. <= s[1:N_STAGES-1] <= 7)
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@setObjective(m, Min, sum{COST[i]*u[i], i = 1:N_STAGES-1})
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@objective(m, Min, sum{COST[i]*u[i], i = 1:N_STAGES-1})
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for i in 1:(N_STAGES-1)
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@addConstraint(m, x[i+1] - x[i] + u[i] + s[i] - alea_year[i] == 0)
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@constraint(m, x[i+1] - x[i] + u[i] + s[i] - alea_year[i] == 0)
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end
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@addConstraint(m, x[1] .==X0)
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@constraint(m, x[1] .==X0)
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status = solve(m)
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println(status)

‎examples/damsvalley.jl‎

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#############################################################################
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srand(2713)
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push!(LOAD_PATH, "../src")
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using StochDynamicProgramming, JuMP, Clp
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m = Model(solver=SOLVER)
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@defVar(m, VOLUME_MIN <= x1[1:N_STAGES] <= VOLUME_MAX)
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@defVar(m, VOLUME_MIN <= x2[1:N_STAGES] <= VOLUME_MAX)
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@defVar(m, CONTROL_MIN <= u1[1:N_STAGES-1] <= CONTROL_MAX)
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@defVar(m, CONTROL_MIN <= u2[1:N_STAGES-1] <= CONTROL_MAX)
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@variable(m, VOLUME_MIN <= x1[1:N_STAGES] <= VOLUME_MAX)
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@variable(m, VOLUME_MIN <= x2[1:N_STAGES] <= VOLUME_MAX)
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@variable(m, CONTROL_MIN <= u1[1:N_STAGES-1] <= CONTROL_MAX)
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@variable(m, CONTROL_MIN <= u2[1:N_STAGES-1] <= CONTROL_MAX)
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@setObjective(m, Min, sum{COST[i]*(u1[i] + u2[i]), i = 1:N_STAGES})
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@objective(m, Min, sum{COST[i]*(u1[i] + u2[i]), i = 1:N_STAGES})
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for i in 1:N_STAGES-1
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@addConstraint(m, x1[i+1] - x1[i] + u1[i] - alea_year[i] == 0)
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@addConstraint(m, x2[i+1] - x2[i] + u2[i] - u1[i] == 0)
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@constraint(m, x1[i+1] - x1[i] + u1[i] - alea_year[i] == 0)
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@constraint(m, x2[i+1] - x2[i] + u2[i] - u1[i] == 0)
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end
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@addConstraint(m, x1[1] == X0[1])
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@addConstraint(m, x2[1] == X0[2])
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@constraint(m, x1[1] == X0[1])
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@constraint(m, x2[1] == X0[2])
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status = solve(m)
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println(status)

‎examples/dynamic_example.jl‎

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#############################################################################
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#srand(2713)
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push!(LOAD_PATH, "../src")
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using StochDynamicProgramming, JuMP, Clp
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#Constant that the user have to define himself
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const SOLVER = ClpSolver()
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# const SOLVER = CplexSolver(CPX_PARAM_SIMDISPLAY=0)
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const N_STAGES = 5
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const N_SCENARIOS = 1
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const N_STAGES = 3
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const N_SCENARIOS = 2
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const DIM_STATES = 1
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const DIM_CONTROLS = 1
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const DIM_CONTROLS = 2
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const DIM_ALEAS = 1
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#Solve the problem and try nb_iter times to generate random data in case of infeasibility
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unsolve = true
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sol = 0
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firstControl = zeros(DIM_CONTROLS*N_SCENARIOS)
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i = 0
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nb_iter = 10
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while i<nb_iter
184-
sol, status = extensive_formulation(model,params)
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sol, firstControl, status = extensive_formulation(model,params)
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if (status == :Optimal)
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unsolve = false
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break
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204204
else
205205
a,b = solve_dams(modelbis,paramsbis)
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println("solution =",sol)
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println("firstControl =", firstControl)
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println("V0 = ", b[1].lambdas[1,:]*X0+b[1].betas[1])
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end
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‎examples/stock-example.jl‎

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# u_min <= u_t <= u_max
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# u_t choosen knowing xi_1 .. xi_t
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#############################################################################
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push!(LOAD_PATH, "../src")
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using StochDynamicProgramming, JuMP, Clp, Distributions
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println("library loaded")
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