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@ -14,8 +14,10 @@ mutable struct JuMPSolverData
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instance
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model
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bin_vars
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solution::Union{Nothing, Dict{String,Float64}}
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solution
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cname_to_constr
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reduced_costs
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dual_values
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end
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@ -26,34 +28,27 @@ Optimizes a given JuMP model while capturing the solver log, then returns that l
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If tee=true, prints the solver log to the standard output as the optimization takes place.
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"""
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function optimize_and_capture_output!(model; tee::Bool=false)
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original_stdout = stdout
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rd, wr = redirect_stdout()
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task = @async begin
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log = ""
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while true
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line = String(readavailable(rd))
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isopen(rd) || break
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log *= String(line)
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if tee
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print(original_stdout, line)
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flush(original_stdout)
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end
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end
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return log
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logname = tempname()
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logfile = open(logname, "w")
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redirect_stdout(logfile) do
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JuMP.optimize!(model)
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Base.Libc.flush_cstdio()
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end
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JuMP.optimize!(model)
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sleep(1)
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redirect_stdout(original_stdout)
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close(rd)
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return fetch(task)
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close(logfile)
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log = String(read(logname))
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rm(logname)
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if tee
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println(log)
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end
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return log
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end
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function solve(
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data::JuMPSolverData;
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tee::Bool=false,
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iteration_cb,
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)::Dict
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iteration_cb=nothing,
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)
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instance, model = data.instance, data.model
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wallclock_time = 0
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log = ""
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@ -78,14 +73,14 @@ function solve(
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lower_bound = primal_bound
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upper_bound = dual_bound
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end
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return Dict(
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"Lower bound" => lower_bound,
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"Upper bound" => upper_bound,
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"Sense" => sense,
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"Wallclock time" => wallclock_time,
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"Nodes" => 1,
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"MIP log" => log,
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"Warm start value" => nothing,
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return miplearn.solvers.internal.MIPSolveStats(
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mip_lower_bound=lower_bound,
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mip_upper_bound=upper_bound,
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mip_sense=sense,
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mip_wallclock_time=wallclock_time,
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mip_nodes=1,
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mip_log=log,
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mip_warm_start_value=nothing,
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)
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end
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@ -97,24 +92,55 @@ function solve_lp(data::JuMPSolverData; tee::Bool=false)
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JuMP.set_upper_bound(var, 1.0)
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JuMP.set_lower_bound(var, 0.0)
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end
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log = optimize_and_capture_output!(model, tee=tee)
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wallclock_time = @elapsed begin
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log = optimize_and_capture_output!(model, tee=tee)
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end
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update_solution!(data)
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obj_value = JuMP.objective_value(model)
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for var in bin_vars
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JuMP.set_binary(var)
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end
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return Dict(
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"LP value" => obj_value,
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"LP log" => log,
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return miplearn.solvers.internal.LPSolveStats(
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lp_value=obj_value,
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lp_log=log,
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lp_wallclock_time=wallclock_time,
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)
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end
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function update_solution!(data::JuMPSolverData)
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data.solution = Dict(
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JuMP.name(var) => JuMP.value(var)
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for var in JuMP.all_variables(data.model)
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)
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vars = JuMP.all_variables(data.model)
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data.solution = [JuMP.value(var) for var in vars]
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# Reduced costs
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if has_duals(data.model)
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data.reduced_costs = []
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for var in vars
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rc = 0.0
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if has_upper_bound(var)
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rc += shadow_price(UpperBoundRef(var))
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end
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if has_lower_bound(var)
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# FIXME: Remove negative sign
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rc -= shadow_price(LowerBoundRef(var))
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end
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if is_fixed(var)
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rc += shadow_price(FixRef(var))
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end
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push!(data.reduced_costs, rc)
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end
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data.dual_values = Dict()
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for (ftype, stype) in JuMP.list_of_constraint_types(data.model)
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for constr in JuMP.all_constraints(data.model, ftype, stype)
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# FIXME: Remove negative sign
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data.dual_values[constr] = -JuMP.dual(constr)
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end
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end
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else
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data.reduced_costs = nothing
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data.dual_values = nothing
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end
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end
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@ -195,6 +221,151 @@ function get_constraint_sense(data::JuMPSolverData, cname)
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end
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function get_variables(
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data::JuMPSolverData;
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with_static::Bool,
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)
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vars = JuMP.all_variables(data.model)
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lb, ub, types, obj_coeffs = nothing, nothing, nothing, nothing
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rc = nothing
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# Variable names
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names = Tuple(JuMP.name.(vars))
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if with_static
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# Lower bounds
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lb = Tuple(
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JuMP.is_binary(v) ? 0.0 :
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JuMP.has_lower_bound(v) ? JuMP.lower_bound(v) :
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-Inf
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for v in vars
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)
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# Upper bounds
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ub = Tuple(
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JuMP.is_binary(v) ? 1.0 :
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JuMP.has_upper_bound(v) ? JuMP.upper_bound(v) :
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Inf
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for v in vars
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)
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# Variable types
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types = Tuple(
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JuMP.is_binary(v) ? "B" :
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JuMP.is_integer(v) ? "I" :
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"C"
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for v in vars
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)
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# Objective function coefficients
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obj = objective_function(data.model)
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obj_coeffs = Tuple(
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v ∈ keys(obj.terms) ? obj.terms[v] : 0.0
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for v in vars
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)
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end
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rc = data.reduced_costs === nothing ? nothing : Tuple(data.reduced_costs)
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values = data.solution === nothing ? nothing : Tuple(data.solution)
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return miplearn.features.VariableFeatures(
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names=names,
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lower_bounds=lb,
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upper_bounds=ub,
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types=types,
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obj_coeffs=obj_coeffs,
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reduced_costs=rc,
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values=values,
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)
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end
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function get_constraints(
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data::JuMPSolverData;
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with_static::Bool,
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)
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names = []
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senses, lhs, rhs = nothing, nothing, nothing
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dual_values = nothing
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if data.dual_values !== nothing
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dual_values = []
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end
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if with_static
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senses, lhs, rhs = [], [], []
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end
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for (ftype, stype) in JuMP.list_of_constraint_types(data.model)
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ftype in [JuMP.AffExpr, JuMP.VariableRef] || error("Unsupported constraint type: ($ftype, $stype)")
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for constr in JuMP.all_constraints(data.model, ftype, stype)
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cset = MOI.get(
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constr.model.moi_backend,
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MOI.ConstraintSet(),
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constr.index,
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)
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name = JuMP.name(constr)
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length(name) > 0 || continue
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push!(names, name)
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if data.dual_values !== nothing
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push!(dual_values, data.dual_values[constr])
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end
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if with_static
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if ftype == JuMP.AffExpr
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push!(
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lhs,
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Tuple(
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(
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MOI.get(
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constr.model.moi_backend,
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MOI.VariableName(),
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term.variable_index
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),
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term.coefficient,
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)
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for term in MOI.get(
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constr.model.moi_backend,
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MOI.ConstraintFunction(),
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constr.index,
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).terms
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)
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)
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if stype == MOI.EqualTo{Float64}
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push!(senses, "=")
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push!(rhs, cset.value)
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elseif stype == MOI.LessThan{Float64}
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push!(senses, "<")
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push!(rhs, cset.upper)
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elseif stype == MOI.GreaterThan{Float64}
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push!(senses, ">")
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push!(rhs, cset.lower)
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else
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error("Unsupported set: $stype")
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end
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else
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error("Unsupported ftype: $ftype")
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end
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end
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end
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end
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function to_tuple(x)
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x !== nothing || return nothing
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return Tuple(x)
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end
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return miplearn.features.ConstraintFeatures(
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names=to_tuple(names),
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senses=to_tuple(senses),
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lhs=to_tuple(lhs),
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rhs=to_tuple(rhs),
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dual_values=to_tuple(dual_values),
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)
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end
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# Constraints: ScalarAffineFunction, LessThan
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# -------------------------------------------------------------------------
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function get_constraint_rhs(
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@ -355,6 +526,23 @@ function get_constraint_sense(
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return "="
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end
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# Test instances
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# ---------------------------------------------
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function build_test_instance_knapsack()
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weights = [23.0, 26.0, 20.0, 18.0]
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prices = [505.0, 352.0, 458.0, 220.0]
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capacity = 67.0
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model = Model()
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n = length(weights)
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@variable(model, x[0:n-1], Bin)
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@variable(model, z, lower_bound=0.0, upper_bound=capacity)
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@objective(model, Max, sum(x[i-1] * prices[i] for i in 1:n))
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@constraint(model, eq_capacity, sum(x[i-1] * weights[i] for i in 1:n) - z == 0)
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return JuMPInstance(model)
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end
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@pydef mutable struct JuMPSolver <: miplearn.solvers.internal.InternalSolver
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function __init__(self; optimizer)
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@ -366,6 +554,8 @@ end
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nothing, # bin_vars
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nothing, # solution
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nothing, # cname_to_constr
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nothing, # reduced_costs
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nothing, # dual_values
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)
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end
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@ -381,9 +571,9 @@ end
|
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solve(
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self;
|
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tee=false,
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|
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iteration_cb,
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lazy_cb,
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user_cut_cb,
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iteration_cb=nothing,
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lazy_cb=nothing,
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user_cut_cb=nothing,
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) = solve(
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self.data,
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tee=tee,
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|
|
@ -396,8 +586,8 @@ end
|
|
|
|
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get_solution(self) =
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self.data.solution
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get_variables(self) =
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get_variables(self.data)
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get_variables(self; with_static=true) =
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get_variables(self.data; with_static=with_static)
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set_branching_priorities(self, priorities) =
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@warn "JuMPSolver: set_branching_priorities not implemented"
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@ -411,6 +601,9 @@ end
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is_infeasible(self) =
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is_infeasible(self.data)
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get_constraints(self; with_static=true) =
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get_constraints(self.data; with_static=with_static)
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get_constraint_ids(self) =
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get_constraint_ids(self.data)
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@ -423,8 +616,45 @@ end
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get_constraint_sense(self, cname) =
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get_constraint_sense(self.data, cname)
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build_test_instance_knapsack(self) =
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build_test_instance_knapsack()
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clone(self) = self
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get_variable_attrs(self) = [
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"names",
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# "basis_status",
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"categories",
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"lower_bounds",
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"obj_coeffs",
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"reduced_costs",
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# "sa_lb_down",
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# "sa_lb_up",
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# "sa_obj_down",
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# "sa_obj_up",
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# "sa_ub_down",
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# "sa_ub_up",
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"types",
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"upper_bounds",
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"user_features",
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"values",
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]
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get_constraint_attrs(self) = [
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# "basis_status",
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"categories",
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"dual_values",
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"lazy",
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"lhs",
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"names",
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"rhs",
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# "sa_rhs_down",
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# "sa_rhs_up",
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"senses",
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# "slacks",
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"user_features",
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]
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add_cut(self) = error("not implemented")
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extract_constraint(self) = error("not implemented")
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is_constraint_satisfied(self) = error("not implemented")
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@ -433,6 +663,11 @@ end
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get_inequality_slacks(self) = error("not implemented")
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get_dual(self) = error("not implemented")
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get_sense(self) = error("not implemented")
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build_test_instance_infeasible(self) = error("not implemented")
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build_test_instance_redundancy(self) = error("not implemented")
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get_constraints_old(self) = error("not implemented")
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is_constraint_satisfied_old(self) = error("not implemented")
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remove_constraint(self) = error("not implemented")
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end
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export JuMPSolver, solve!, fit!, add!
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