WIP: entropic paper

papers/entropic/.gitignore

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+solution_*.j
+stage_objective*.csv
+*.pdf
+*.dat

papers/entropic/Project.toml

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+[deps]
+DelimitedFiles = "8bb1440f-4735-579b-a4ab-409b98df4dab"
+Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
+Gurobi = "2e9cd046-0924-5485-92f1-d5272153d98b"
+JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
+Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
+SDDP = "f4570300-c277-11e8-125c-4912f86ce65d"
+Serialization = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
+Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
+StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd"

papers/entropic/README.md

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+# Multistage stochastic programs with the entropic risk measure
+
+The code in this file runs the examples from the working paper
+
+Dowson, O., Morton, D.P., and Pagnoncelli, B.K., Multistage stochastic programs
+with the entropic risk measure.
+
+[http://www.optimization-online.org/DB_HTML/2020/08/7984.html](http://www.optimization-online.org/DB_HTML/2020/08/7984.html)

papers/entropic/model.jl

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+#  Copyright 2019-21, Oscar Dowson, Lingquan Ding (@lingquant).
+#  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/.
+
+# This example is based on one from MSPPy:
+# https://github.com/lingquant/msppy/blob/dc85a2e8fa5243b3d5096d59085d9caad3ff2ede/examples/hydro_thermal/julia/test.jl
+#
+# The original author was Lingquan Ding (@lingquant), but it was modified by
+# Oscar Dowson (@odow) to meet the latest SDDP.jl syntax.
+#
+# The original model and data is from:
+#
+# Shapiro, A., Tekaya, W., da Costa, J. P., & Soares, M. P. (2013). Risk neutral
+# and risk averse stochastic dual dynamic programming method. European journal
+# of operational research, 224(2), 375–391.
+
+using SDDP
+
+import DelimitedFiles
+import Gurobi
+import JSON
+import Plots
+import Random
+import Statistics
+import StatsPlots
+
+function _build_model(data::Dict)
+    N_THERMAL = length.(data["thermal_obj"])
+    @assert N_THERMAL == length.(data["thermal_lb"])
+    @assert N_THERMAL == length.(data["thermal_ub"])
+    I = [1, 2, 3, 4]
+    K(i) = 1:N_THERMAL[i]
+    IM = 5
+    I_IM = union(I, IM)
+    env = Gurobi.Env()
+    function gurobi_optimizer()
+        model = Gurobi.Optimizer(env)
+        MOI.set(model, MOI.Silent(), true)
+        return model
+    end
+    model = SDDP.LinearPolicyGraph(
+        stages = 60,
+        lower_bound = 0.0,
+        optimizer = gurobi_optimizer,
+    ) do sp, t
+        set_silent(sp)
+        month = t % 12 == 0 ? 12 : t % 12
+        @variable(
+            sp,
+            0 <= v[i in I] <= data["storedEnergy_ub"][i] / 1_000,
+            SDDP.State,
+            initial_value = data["storedEnergy_initial"][i] / 1_000,
+        )
+        @variable(sp, s[i in I] >= 0.0)
+        @variable(sp, 0.0 <= q[i in I] <= data["hydro_ub"][i] / 1_000)
+        @variable(
+            sp,
+            g[i in I, k in K(i)],
+            lower_bound = data["thermal_lb"][i][k] / 1_000,
+            upper_bound = data["thermal_ub"][i][k] / 1_000,
+        )
+        @variable(
+            sp,
+            0 <= ex[i in I_IM, j in I_IM] <= data["exchange_ub"][i][j] / 1_000,
+        )
+        @variable(
+            sp,
+            df[i in I, j in I] >= 0,
+            upper_bound =
+                data["demand"][month][i] * data["deficit_ub"][j] / 1_000,
+        )
+        @stageobjective(
+            sp,
+            sum(
+                data["deficit_obj"][i] * sum(df[i, :]) +
+                sum(data["thermal_obj"][i][k] * g[i, k] for k in K(i)) for
+                i in I
+            )
+        )
+        @constraint(
+            sp,
+            [i in I],
+            q[i] + sum(g[i, k] for k in K(i)) + sum(df[i, :]) + sum(ex[:, i]) - sum(ex[i, :]) ==
+            data["demand"][month][i] / 1_000
+        )
+        @constraint(
+            sp,
+            balance[i in I],
+            v[i].out ==
+            v[i].in + data["inflow_initial"][i] / 1_000 - s[i] - q[i]
+        )
+        @constraint(sp, sum(ex[:, IM]) == sum(ex[IM, :]))
+        if t == 1
+            # Deterministic first stage with `inflow_initial`.
+        else
+            r = (t - 1) % 12 == 0 ? 12 : (t - 1) % 12
+            Ω = data["scenarios"]
+            # To simplify things. Don't use all the scenarios!
+            num_scenarios = min(40, length(Ω[1][r]))
+            SDDP.parameterize(sp, 1:num_scenarios) do ω
+                for i in 1:4
+                    set_normalized_rhs(balance[i], Ω[i][r][ω] / 1_000)
+                end
+            end
+        end
+    end
+    return model
+end
+
+function _train_model(model::SDDP.PolicyGraph, gamma::Float64)
+    risk_measure = isfinite(gamma) ? SDDP.Entropic(gamma) : SDDP.WorstCase()
+    # Set the same random seed for all runs!
+    Random.seed!(1234)
+    psr_sampling_scheme = SDDP.PSRSamplingScheme(1_000)
+    try
+        SDDP.train(
+            model;
+            iteration_limit = 10_000,
+            risk_measure = risk_measure,
+            sampling_scheme = psr_sampling_scheme,
+            log_file = "$(gamma).log",
+        )
+    catch ex
+        @info "Ignorring error"
+        println("$(ex)")
+    end
+    # Set the same random seed for all runs!
+    Random.seed!(12345)
+    simulations = SDDP.simulate(
+        model,
+        1_000,
+        [:v, :df, :g];
+        sampling_scheme = psr_sampling_scheme,
+    )
+    stage_objectives = [
+        round(simulations[i][t][:stage_objective]; digits = 1) for
+        i in 1:length(simulations) for t in 1:length(simulations[i])
+    ]
+    stage_objectives =
+        reshape(stage_objectives, length(simulations[1]), length(simulations))
+    open("stage_objectives_$(gamma).csv", "w") do io
+        return DelimitedFiles.writedlm(
+            io,
+            collect(transpose(stage_objectives)),
+            ',',
+        )
+    end
+    return
+end
+
+function _plot_cumulative_density(
+    filenames = [
+        "stage_objectives_0.000000.csv",
+        "stage_objectives_0.000010.csv",
+        "stage_objectives_5.0e-5.csv",
+        "stage_objectives_0.000100.csv",
+    ],
+)
+    Plots.plot()
+    line_styles = [:solid, :dash, :dot, :dashdot]
+    for (i, f) in enumerate(filenames)
+        X = sum(DelimitedFiles.readdlm(f, ','); dims = 2)[:]
+        x = parse(Float64, match(r"stage\_objectives\_(.+)\.csv", f)[1])
+        x = if x ≈ 0.0
+            "0"
+        elseif x ≈ 5e-5
+            "5 \\times 10^{-5}"
+        else
+            "1 \\times 10^{$(Int(log10(x)))}"
+        end
+        StatsPlots.cdensity!(
+            X,
+            label = "\$\\gamma = $(x)\$",
+            style = line_styles[i],
+            color = :black,
+        )
+    end
+    p = Plots.plot!(
+        xlabel = "Cost [\$]",
+        ylabel = "Cumulative density",
+        legend = :bottomright,
+        size = (400, 300),
+    )
+    Plots.savefig("cumulative_density.pdf")
+    return p
+end
+
+function _plot_objectives(filename::String)
+    quantiles = [0.0, 0.01, 0.05, 0.5, 0.95, 0.99, 1.0]
+    matrix = DelimitedFiles.readdlm(filename, ',')
+    A = mapreduce(
+        i -> Statistics.quantile(matrix[:, i], quantiles)',
+        vcat,
+        1:size(matrix, 2),
+    )
+    x = parse(Float64, match(r"stage\_objectives\_(.+)\.csv", filename)[1])
+    x = if x ≈ 0.0
+        "0"
+    elseif x ≈ 5e-5
+        "5 \\times 10^{-5}"
+    else
+        "10^{$(Int(log10(x)))}"
+    end
+    Plots.plot(
+        A[:, 4],
+        ribbon = (A[:, 4] .- A[:, 1], A[:, 7] .- A[:, 4]),
+        color = :black,
+        fillalpha = 0.2,
+        legend = false,
+        title = "\$\\gamma = $(x)\$",
+        size = (300, 400),
+    )
+    Plots.plot!(
+        A[:, 4],
+        ribbon = (A[:, 4] .- A[:, 2], A[:, 6] .- A[:, 4]),
+        color = :black,
+        fillalpha = 0.2,
+    )
+    Plots.plot!(
+        A[:, 4],
+        ribbon = (A[:, 4] .- A[:, 3], A[:, 5] .- A[:, 4]),
+        color = :black,
+        fillalpha = 0.2,
+    )
+    return Plots.plot!(
+        A[:, 4],
+        color = :black,
+        xlabel = "Stages",
+        ylabel = "Stage objective (\$)",
+        ylim = (0, 4e4),
+    )
+end
+
+function _plot_objectives(
+    filenames::Vector{String} = [
+        "stage_objectives_0.000000.csv",
+        "stage_objectives_0.000010.csv",
+        "stage_objectives_5.0e-5.csv",
+        "stage_objectives_0.000100.csv",
+    ],
+)
+    p = Plots.plot(
+        _plot_objectives.(filenames)...,
+        size = (1200, 800),
+        margin = 5Plots.mm,
+    )
+    Plots.savefig("objectives.pdf")
+    return p
+end
+
+function _print_help()
+    return println(
+        """
+usage: julia [-p N] [--project=.] model.jl [--gamma=<value>] [--help] [--plot]
+
+Solve the hydro-thermal scheduling problem with the Entropic risk measure
+parameterized with γ = <value>.
+
+Use `-p N` to run the SDDP solver in parallel mode over `N` processors.
+
+Use `--project=.` to reproduce the example in the paper using the provided
+`Manifest.toml` and `Project.toml` files.
+
+Examples:
+
+    julia model.jl --gamma=0.5
+    julia -p 4 --project=. model.jl --gamma=0.5
+    julia -p 4 --project=. model.jl --gamma=5.0
+    julia --project=. model.jl --plot
+""",
+    )
+end
+
+function main(args)
+    if length(args) == 0
+        _print_help()
+    elseif findfirst(isequal("--help"), args) !== nothing
+        _print_help()
+    elseif findfirst(isequal("-h"), args) !== nothing
+        _print_help()
+    else
+        i = findfirst(arg -> startswith(arg, "--gamma="), args)
+        if i !== nothing
+            data = JSON.parsefile(joinpath(@__DIR__, "data.json"))
+            model = _build_model(data)
+            gamma = parse(Float64, replace(args[i], "--gamma=" => ""))::Float64
+            _train_model(model, gamma)
+        end
+        if findfirst(isequal("--plot"), args) !== nothing
+            _plot_cumulative_density()
+            _plot_objectives()
+        end
+    end
+end
+
+# ============================================================================ #
+#                              Script entry point!                             #
+# ============================================================================ #
+
+main(ARGS)