google-research

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import FirstOrderLp
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import CSV
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include("../src/PrimalDualMethods.jl")
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include("script_parameters.jl")
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include("../src/choose_primal_weight.jl")
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# Parse command line arguements
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@assert length(ARGS) == 9
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test_problem_folder = ARGS[1]
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results_csv_file = ARGS[2]
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@assert length(results_csv_file) > 3
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@assert results_csv_file[end-3:end] == ".csv"
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method = RecoverPrimalDualMethodFromString(ARGS[3])
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problem_name = ARGS[4]
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restart_scheme_name_arg = ARGS[5]
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restart_length = parse(Int64, ARGS[6])
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if ARGS[7] == "yes"
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  always_reset_to_average = true
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elseif ARGS[7] == "no"
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  always_reset_to_average = false
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else
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  error("unknown value for reset to average")
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end
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if restart_scheme_name_arg == "no_restarts"
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  restart_scheme = NoRestarts()
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elseif restart_scheme_name_arg == "fixed_frequency"
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  restart_scheme =
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    FixedFrequencyRestarts(restart_length, always_reset_to_average)
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elseif restart_scheme_name_arg == "adaptive"
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  restart_scheme = AdaptiveRestarts(exp(-1), always_reset_to_average)
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else
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  error("unknown restart scheme")
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end
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iteration_limit = parse(Int64, ARGS[8])
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kkt_tolerance = parse(Float64, ARGS[9])
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println("OPTIONS SELECTED:")
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@show test_problem_folder
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@show results_csv_file
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@show method
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@show problem_name
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@show restart_scheme
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@show iteration_limit
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@show kkt_tolerance
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params = PrimalDualOptimizerParameters(
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  method,
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  nothing, # step_size (forces the solver to use a provably correct step size)
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  1.0, # primal_weight
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  30, # record every
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  100, # print every
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  true, # verbose
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  ITERATION_LIMIT_FOR_FINDING_BEST_PRIMAL_WEIGHT, # iteration limit
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  NoRestarts(), # restart scheme
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  nothing, # don't record information on subspace
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  kkt_tolerance,
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)
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instance_path = joinpath(test_problem_folder, "$(problem_name).mps.gz")
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lp = FirstOrderLp.qps_reader_to_standard_form(instance_path)
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lp.variable_upper_bound .= Inf
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if method == ADMM
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  convert_problem_to_standard_form!(lp)
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end
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println("FIND BEST PRIMAL WEIGHT")
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best_primal_weight =
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  find_best_primal_weight(params, lp, 4.0 .^ collect(-5:5), 1.0, 1.0)
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println("best_primal_weight = $best_primal_weight")
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println("RUN ALGORITHM")
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params.restart_scheme = restart_scheme
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params.iteration_limit = iteration_limit
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params.primal_weight = best_primal_weight
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solver_output = optimize(params, lp)
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CSV.write(results_csv_file, solver_output.iteration_stats)
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