FisSal2011: Adjust constants

Project.toml

@@ -1,11 +1,12 @@
 name = "MIPLearn"
 uuid = "2b1277c3-b477-4c49-a15e-7ba350325c68"
 authors = ["Alinson S Xavier <git@axavier.org>"]
-version = "0.4.0"
+version = "0.4.2"
 
 [deps]
 Conda = "8f4d0f93-b110-5947-807f-2305c1781a2d"
 DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
+Gurobi = "2e9cd046-0924-5485-92f1-d5272153d98b"
 HDF5 = "f67ccb44-e63f-5c2f-98bd-6dc0ccc4ba2f"
 HiGHS = "87dc4568-4c63-4d18-b0c0-bb2238e4078b"
 JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819"
@@ -28,6 +29,7 @@ TimerOutputs = "a759f4b9-e2f1-59dc-863e-4aeb61b1ea8f"
 [compat]
 Conda = "1"
 DataStructures = "0.18"
+Gurobi = "1.7.5"
 HDF5 = "0.16"
 HiGHS = "1"
 JLD2 = "0.4"
@@ -36,8 +38,10 @@ JuMP = "1"
 KLU = "0.4"
 MathOptInterface = "1"
 OrderedCollections = "1"
+PrecompileTools = "1"
 PyCall = "1"
 Requires = "1"
+SCIP = "0.12"
 Statistics = "1"
 TimerOutputs = "0.5"
 julia = "1"

deps/build.jl

@@ -5,7 +5,7 @@ function install_miplearn()
     Conda.update()
     pip = joinpath(dirname(pyimport("sys").executable), "pip")
     isfile(pip) || error("$pip: invalid path")
-    run(`$pip install miplearn==0.4.0`)
+    run(`$pip install miplearn==0.4.4`)
 end
 
 install_miplearn()

src/BB/log.jl

@@ -6,7 +6,7 @@ using Printf
 
 function print_progress_header()
     @printf(
-        "%8s %9s %9s %13s %13s %9s %6s %13s %6s %-24s %9s %9s %6s %6s",
+        "%8s %9s %9s %13s %13s %9s %9s %13s %9s %-24s %9s %9s %6s %6s",
         "time",
         "processed",
         "pending",
@@ -31,9 +31,9 @@ function print_progress(
     node::Node;
     time_elapsed::Float64,
     print_interval::Int,
-    primal_update::Bool
+    primal_update::Bool,
 )::Nothing
-    if (pool.processed % print_interval == 0) || isempty(pool.pending)
+    if (pool.processed % print_interval == 0) || isempty(pool.pending) || primal_update
         if isempty(node.branch_vars)
             branch_var_name = "---"
             branch_lb = "---"
@@ -46,7 +46,7 @@ function print_progress(
             branch_ub = @sprintf("%9.2f", last(node.branch_ub))
         end
         @printf(
-            "%8.2f %9d %9d %13.6e %13.6e %9.2e %6d %13.6e %6s %-24s %9s %9s %6d %6d",
+            "%8.2f %9d %9d %13.6e %13.6e %9.2e %9d %13.6e %9s %-24s %9s %9s %6d %6d",
             time_elapsed,
             pool.processed,
             length(pool.processing) + length(pool.pending),

src/BB/lp.jl

@@ -134,7 +134,11 @@ function _get_int_variables(
                 var_ub = constr.upper
                 MOI.delete(optimizer, _upper_bound_index(var))
             end
-            MOI.add_constraint(optimizer, var, MOI.Interval(var_lb, var_ub))
+            MOI.add_constraint(
+                optimizer,
+                MOI.VariableIndex(var.index),
+                MOI.Interval(var_lb, var_ub),
+            )
         end
         push!(vars, var)
         push!(lb, var_lb)

src/BB/nodepool.jl

@@ -8,10 +8,10 @@ import Base.Threads: threadid
 
 function take(
     pool::NodePool;
-    suggestions::Array{Node}=[],
+    suggestions::Array{Node} = [],
     time_remaining::Float64,
     gap_limit::Float64,
-    node_limit::Int
+    node_limit::Int,
 )::Union{Symbol,Node}
     t = threadid()
     lock(pool.lock) do
@@ -53,8 +53,8 @@ function offer(
     pool::NodePool;
     parent_node::Union{Nothing,Node},
     child_nodes::Vector{Node},
-    time_elapsed::Float64=0.0,
+    time_elapsed::Float64 = 0.0,
-    print_interval::Int=100
+    print_interval::Int = 100,
 )::Nothing
     lock(pool.lock) do
         primal_update = false
@@ -101,16 +101,15 @@ function offer(
             # Update branching variable history
             branch_var = child_nodes[1].branch_vars[end]
             offset = findfirst(isequal(branch_var), parent_node.fractional_variables)
-            if offset !== nothing
             x = parent_node.fractional_values[offset]
             obj_change_up = child_nodes[1].obj - parent_node.obj
             obj_change_down = child_nodes[2].obj - parent_node.obj
             _update_var_history(
-                    pool=pool,
+                pool = pool,
-                    var=branch_var,
+                var = branch_var,
-                    x=x,
+                x = x,
-                    obj_change_down=obj_change_down,
+                obj_change_down = obj_change_down,
-                    obj_change_up=obj_change_up,
+                obj_change_up = obj_change_up,
             )
             # Update global history
             pool.history.avg_pseudocost_up =
@@ -118,15 +117,14 @@ function offer(
             pool.history.avg_pseudocost_down =
                 mean(vh.pseudocost_down for vh in values(pool.var_history))
         end
-        end
 
         for node in child_nodes
             print_progress(
                 pool,
                 node,
-                time_elapsed=time_elapsed,
+                time_elapsed = time_elapsed,
-                print_interval=print_interval,
+                print_interval = print_interval,
-                primal_update=isfinite(node.obj) && isempty(node.fractional_variables),
+                primal_update = isfinite(node.obj) && isempty(node.fractional_variables),
             )
         end
     end
@@ -138,7 +136,7 @@ function _update_var_history(;
     var::Variable,
     x::Float64,
     obj_change_down::Float64,
-    obj_change_up::Float64
+    obj_change_up::Float64,
 )::Nothing
     # Create new history entry
     if var ∉ keys(pool.var_history)

src/BB/optimize.jl

@@ -10,22 +10,16 @@ import ..H5File
 
 function solve!(
     mip::MIP;
-    time_limit::Float64=Inf,
+    time_limit::Float64 = Inf,
-    node_limit::Int=typemax(Int),
+    node_limit::Int = typemax(Int),
-    gap_limit::Float64=1e-4,
+    gap_limit::Float64 = 1e-4,
-    print_interval::Int=5,
+    print_interval::Int = 5,
-    initial_primal_bound::Float64=Inf,
+    initial_primal_bound::Float64 = Inf,
-    branch_rule::VariableBranchingRule=ReliabilityBranching(),
+    branch_rule::VariableBranchingRule = ReliabilityBranching(),
-    enable_plunging=true,
+    enable_plunging = true,
-    replay=nothing
+)::NodePool
-)::Tuple{NodePool,ReplayInfo}
-
-    if replay === nothing
-        replay = ReplayInfo()
-    end
-
     time_initial = time()
-    pool = NodePool(mip=mip, next_index=replay.next_index)
+    pool = NodePool(mip = mip)
     pool.primal_bound = initial_primal_bound
 
     root_node = _create_node(mip)
@@ -40,9 +34,9 @@ function solve!(
 
     offer(
         pool,
-        parent_node=nothing,
+        parent_node = nothing,
-        child_nodes=[root_node],
+        child_nodes = [root_node],
-        print_interval=print_interval,
+        print_interval = print_interval,
     )
     @threads for t = 1:nthreads()
         child_one, child_zero, suggestions = nothing, nothing, Node[]
@@ -53,10 +47,10 @@ function solve!(
             end
             node = take(
                 pool,
-                suggestions=suggestions,
+                suggestions = suggestions,
-                time_remaining=time_limit - time_elapsed,
+                time_remaining = time_limit - time_elapsed,
-                node_limit=node_limit,
+                node_limit = node_limit,
-                gap_limit=gap_limit,
+                gap_limit = gap_limit,
             )
             if node == :END
                 break
@@ -70,25 +64,10 @@ function solve!(
                 @assert status == :Optimal
                 _unset_node_bounds(node)
 
-                if node.index in keys(replay.node_decisions)
-                    decision = replay.node_decisions[node.index]
-                    ids = decision.ids
-                    branch_var = decision.branch_var
-                    var_value = decision.var_value
-                else
                 # Find branching variable
                 ids = generate_indices(pool, 2)
                 branch_var = find_branching_var(branch_rule, node, pool)
 
-                    # Query current fractional value
-                    offset = findfirst(isequal(branch_var), node.fractional_variables)
-                    var_value = node.fractional_values[offset]
-
-                    # Update replay
-                    decision = ReplayNodeDecision(; branch_var, var_value, ids)
-                    replay.node_decisions[node.index] = decision
-                end
-
                 # Find current variable lower and upper bounds
                 offset = findfirst(isequal(branch_var), mip.int_vars)
                 var_lb = mip.int_vars_lb[offset]
@@ -100,43 +79,46 @@ function solve!(
                     end
                 end
 
+                # Query current fractional value
+                offset = findfirst(isequal(branch_var), node.fractional_variables)
+                var_value = node.fractional_values[offset]
+
                 child_zero = _create_node(
                     mip,
-                    index=ids[2],
+                    index = ids[2],
-                    parent=node,
+                    parent = node,
-                    branch_var=branch_var,
+                    branch_var = branch_var,
-                    branch_var_lb=var_lb,
+                    branch_var_lb = var_lb,
-                    branch_var_ub=floor(var_value),
+                    branch_var_ub = floor(var_value),
                 )
                 child_one = _create_node(
                     mip,
-                    index=ids[1],
+                    index = ids[1],
-                    parent=node,
+                    parent = node,
-                    branch_var=branch_var,
+                    branch_var = branch_var,
-                    branch_var_lb=ceil(var_value),
+                    branch_var_lb = ceil(var_value),
-                    branch_var_ub=var_ub,
+                    branch_var_ub = var_ub,
                 )
                 offer(
                     pool,
-                    parent_node=node,
+                    parent_node = node,
-                    child_nodes=[child_one, child_zero],
+                    child_nodes = [child_one, child_zero],
-                    time_elapsed=time_elapsed,
+                    time_elapsed = time_elapsed,
-                    print_interval=print_interval,
+                    print_interval = print_interval,
                 )
             end
         end
     end
-    replay.next_index = pool.next_index
+    return pool
-    return pool, replay
 end
 
 function _create_node(
     mip;
-    index::Int=0,
+    index::Int = 0,
-    parent::Union{Nothing,Node}=nothing,
+    parent::Union{Nothing,Node} = nothing,
-    branch_var::Union{Nothing,Variable}=nothing,
+    branch_var::Union{Nothing,Variable} = nothing,
-    branch_var_lb::Union{Nothing,Float64}=nothing,
+    branch_var_lb::Union{Nothing,Float64} = nothing,
-    branch_var_ub::Union{Nothing,Float64}=nothing
+    branch_var_ub::Union{Nothing,Float64} = nothing,
 )::Node
     if parent === nothing
         branch_vars = Variable[]

src/BB/structs.jl

@@ -72,14 +72,3 @@ Base.@kwdef mutable struct NodePool
     history::History = History()
     var_history::Dict{Variable,VariableHistory} = Dict()
 end
-
-Base.@kwdef struct ReplayNodeDecision
-    branch_var
-    var_value
-    ids
-end
-
-Base.@kwdef mutable struct ReplayInfo
-    node_decisions::Dict{Int,ReplayNodeDecision} = Dict()
-    next_index::Int = 1
-end

src/Cuts/tableau/gmi.jl

@@ -185,7 +185,7 @@ function collect_gmi(
     )
 end
 
-function select_gmi_rows(data, basis, x; max_rows = 10, atol = 1e-4)
+function select_gmi_rows(data, basis, x; max_rows = 10, atol = 0.001)
     candidate_rows = [
         r for r = 1:length(basis.var_basic) if (
             (data.var_types[basis.var_basic[r]] != 'C') &&
@@ -199,23 +199,82 @@ function select_gmi_rows(data, basis, x; max_rows = 10, atol = 1e-4)
     return [candidate_rows[perm[i]] for i = 1:min(length(perm), max_rows)]
 end
 
+# function compute_gmi(data::ProblemData, tableau::Tableau)::ConstraintSet
+#     @timeit "Initialization" begin
+#         nrows, ncols = size(tableau.lhs)
+#         ub = Float64[Inf for _ = 1:nrows]
+#         lb = Float64[0.999 for _ = 1:nrows]
+#         tableau_I, tableau_J, tableau_V = findnz(tableau.lhs)
+#         lhs_I = Int[]
+#         lhs_J = Int[]
+#         lhs_V = Float64[]
+#     end
+#     @timeit "Compute coefficients" begin
+#         for k = 1:nnz(tableau.lhs)
+#             i::Int = tableau_I[k]
+#             j::Int = tableau_J[k]
+#             v::Float64 = 0.0
+#             frac_alpha_j = frac(tableau_V[k])
+#             alpha_j = tableau_V[k]
+#             beta = frac(tableau.rhs[i])
+#             if data.var_types[j] == 'C'
+#                 if alpha_j >= 0
+#                     v = alpha_j / beta
+#                 else
+#                     v = -alpha_j / (1 - beta)
+#                 end
+#             else
+#                 if frac_alpha_j < beta
+#                     v = frac_alpha_j / beta
+#                 else
+#                     v = (1 - frac_alpha_j) / (1 - beta)
+#                 end
+#             end
+#             if abs(v) > 1e-8
+#                 push!(lhs_I, i)
+#                 push!(lhs_J, tableau_J[k])
+#                 push!(lhs_V, v)
+#             end
+#         end
+#     end
+#     @timeit "Convert to ConstraintSet" begin
+#         lhs = sparse(lhs_I, lhs_J, lhs_V, nrows, ncols)
+#         cs = ConstraintSet(; lhs, ub, lb)
+#     end
+#     return cs
+# end
+
 function compute_gmi(data::ProblemData, tableau::Tableau)::ConstraintSet
-    nrows, ncols = size(tableau.lhs)
+    @timeit "Initialization" begin
-    ub = Float64[Inf for _ = 1:nrows]
+        nrows::Int, ncols::Int = size(tableau.lhs)
-    lb = Float64[0.9999 for _ = 1:nrows]
+        var_types::Vector{Char} = data.var_types
-    tableau_I, tableau_J, tableau_V = findnz(tableau.lhs)
+        tableau_rhs::Vector{Float64} = tableau.rhs
-    lhs_I = Int[]
+        tableau_I::Vector{Int}, tableau_J::Vector{Int}, tableau_V::Vector{Float64} = findnz(tableau.lhs)
-    lhs_J = Int[]
+    end
-    lhs_V = Float64[]
+
+    @timeit "Pre-allocation" begin
+        cut_ub::Vector{Float64} = fill(Inf, nrows)
+        cut_lb::Vector{Float64} = fill(0.999, nrows)
+        nnz_tableau::Int = length(tableau_I)
+        cut_lhs_I::Vector{Int} = Vector{Int}(undef, nnz_tableau)
+        cut_lhs_J::Vector{Int} = Vector{Int}(undef, nnz_tableau)
+        cut_lhs_V::Vector{Float64} = Vector{Float64}(undef, nnz_tableau)
+        cut_hash::Vector{UInt64} = zeros(UInt64, nrows)
+        nnz_count::Int = 0
+    end
+    
     @timeit "Compute coefficients" begin
-        for k = 1:nnz(tableau.lhs)
+        @inbounds for k = 1:nnz_tableau
             i::Int = tableau_I[k]
             j::Int = tableau_J[k]
-            v::Float64 = 0.0
+            alpha_j::Float64 = tableau_V[k]
-            frac_alpha_j = frac(tableau_V[k])
+            frac_alpha_j::Float64 = alpha_j - floor(alpha_j)
-            alpha_j = tableau_V[k]
+            beta_i::Float64 = tableau_rhs[i]
-            beta = frac(tableau.rhs[i])
+            beta::Float64 = beta_i - floor(beta_i)
-            if data.var_types[j] == 'C'
+            v::Float64 = 0
+            
+            # Compute coefficient
+            if var_types[j] == 'C'
                 if alpha_j >= 0
                     v = alpha_j / beta
                 else
@@ -228,16 +287,34 @@ function compute_gmi(data::ProblemData, tableau::Tableau)::ConstraintSet
                     v = (1 - frac_alpha_j) / (1 - beta)
                 end
             end
+            
+            # Store if significant
             if abs(v) > 1e-8
-                push!(lhs_I, i)
+                nnz_count += 1
-                push!(lhs_J, tableau_J[k])
+                cut_lhs_I[nnz_count] = i
-                push!(lhs_V, v)
+                cut_lhs_J[nnz_count] = j
+                cut_lhs_V[nnz_count] = v
+                cut_hash[i] = hash(j, cut_hash[i])
+                cut_hash[i] = hash(v, cut_hash[i])
             end
         end
-        lhs = sparse(lhs_I, lhs_J, lhs_V, nrows, ncols)
     end
-    return ConstraintSet(; lhs, ub, lb)
+
+    @timeit "Resize arrays to actual size" begin
+        resize!(cut_lhs_I, nnz_count)
+        resize!(cut_lhs_J, nnz_count)
+        resize!(cut_lhs_V, nnz_count)
+    end
+
+    # TODO: Build cut in compressed row format instead of converting
+    @timeit "Convert to ConstraintSet" begin
+        cut_lhs::SparseMatrixCSC = sparse(cut_lhs_I, cut_lhs_J, cut_lhs_V, nrows, ncols)
+        cs::ConstraintSet = ConstraintSet(; lhs=cut_lhs, ub=cut_ub, lb=cut_lb, hash=cut_hash)
+    end
+    
+    return cs
 end
 
+
 export compute_gmi,
     frac, select_gmi_rows, assert_cuts_off, assert_does_not_cut_off, collect_gmi

src/Cuts/tableau/numerics.jl

@@ -27,25 +27,26 @@ function assert_eq(a, b; atol = 1e-4)
 end
 
 function assert_cuts_off(cuts::ConstraintSet, x::Vector{Float64}, tol = 1e-6)
+    vals = cuts.lhs * x
     for i = 1:length(cuts.lb)
-        val = cuts.lhs[i, :]' * x
+        if (vals[i] <= cuts.ub[i] - tol) && (vals[i] >= cuts.lb[i] + tol)
-        if (val <= cuts.ub[i] - tol) && (val >= cuts.lb[i] + tol)
+            throw(ErrorException("inequality $i fails to cut off fractional solution: $(cuts.lb[i]) <= $(vals[i]) <= $(cuts.ub[i])"))
-            throw(ErrorException("inequality fails to cut off fractional solution"))
         end
     end
 end
 
 function assert_does_not_cut_off(cuts::ConstraintSet, x::Vector{Float64}; tol = 1e-6)
+    vals = cuts.lhs * x
     for i = 1:length(cuts.lb)
-        val = cuts.lhs[i, :]' * x
+        if (vals[i] >= cuts.ub[i]) || (vals[i] <= cuts.lb[i])
-        ub = cuts.ub[i]
+            throw(ErrorException("inequality $i cuts off integer solution: $(cuts.lb[i]) <= $(vals[i]) <= $(cuts.ub[i])"))
-        lb = cuts.lb[i]
-        if (val >= ub) || (val <= lb)
-            throw(
-                ErrorException(
-                    "inequality $i cuts off integer solution ($lb <= $val <= $ub)",
-                ),
-            )
         end
     end
 end
+
+function assert_int(x::Float64, tol=1e-5)
+    fx = frac(x)
+    if min(fx, 1 - fx) >= tol
+        throw(ErrorException("Number must be integer: $x"))
+    end
+end

src/Cuts/tableau/structs.jl

@@ -18,10 +18,10 @@ Base.@kwdef mutable struct ProblemData
 end
 
 Base.@kwdef mutable struct Tableau
-    obj::Any
+    obj::Vector{Float64}
-    lhs::Any
+    lhs::SparseMatrixCSC
-    rhs::Any
+    rhs::Vector{Float64}
-    z::Any
+    z::Float64
 end
 
 Base.@kwdef mutable struct Basis
@@ -35,6 +35,7 @@ Base.@kwdef mutable struct ConstraintSet
     lhs::SparseMatrixCSC
     ub::Vector{Float64}
     lb::Vector{Float64}
+    hash::Union{Nothing,Vector{UInt64}} = nothing
 end
 
 export ProblemData, Tableau, Basis, ConstraintSet

src/Cuts/tableau/tableau.jl

@@ -4,14 +4,24 @@
 
 using KLU
 using TimerOutputs
+using Gurobi
 
 function get_basis(model::JuMP.Model)::Basis
+    if isa(unsafe_backend(model), Gurobi.Optimizer)
+        return get_basis_gurobi(model)
+    end
+
+    @timeit "Initialization" begin
         var_basic = Int[]
         var_nonbasic = Int[]
         constr_basic = Int[]
         constr_nonbasic = Int[]
+        nvars = num_variables(model)
+        sizehint!(var_basic, nvars)
+        sizehint!(var_nonbasic, nvars)
+    end
 
-    # Variables
+    @timeit "Query variables" begin
         for (i, var) in enumerate(all_variables(model))
             bstatus = MOI.get(model, MOI.VariableBasisStatus(), var)
             if bstatus == MOI.BASIC
@@ -22,8 +32,9 @@ function get_basis(model::JuMP.Model)::Basis
                 error("Unknown basis status: $bstatus")
             end
         end
+    end
 
-    # Constraints
+    @timeit "Query constraints" begin
         constr_index = 1
         for (ftype, stype) in list_of_constraint_types(model)
             for constr in all_constraints(model, ftype, stype)
@@ -44,8 +55,110 @@ function get_basis(model::JuMP.Model)::Basis
                 end
             end
         end
+    end
 
-    return Basis(; var_basic, var_nonbasic, constr_basic, constr_nonbasic)
+    @timeit "Build basis struct" begin
+        basis = Basis(; var_basic, var_nonbasic, constr_basic, constr_nonbasic)
+    end
+
+    return basis
+end
+
+function set_basis(model::JuMP.Model, basis::Basis)
+    if isa(unsafe_backend(model), Gurobi.Optimizer)
+        # NOP
+        return
+    end
+
+    @timeit "Initialization" begin
+        nvars = num_variables(model)
+        gurobi_model = unsafe_backend(model).inner
+    end
+
+    @timeit "Set variable basis" begin
+        var_basis_statuses = Vector{Cint}(undef, nvars)
+        fill!(var_basis_statuses, -1)  # Default to GRB_NONBASIC_LOWER
+        
+        for var_idx in basis.var_basic
+            var_basis_statuses[var_idx] = 0  # GRB_BASIC
+        end
+        
+        ret = GRBsetintattrarray(gurobi_model, "VBasis", 0, nvars, var_basis_statuses)
+        if ret != 0
+            error("Failed to set variable basis statuses in Gurobi: error code $ret")
+        end
+    end
+
+    @timeit "Set constraint basis" begin
+        nconstr = num_constraints(model, AffExpr, MOI.EqualTo{Float64})
+        constr_basis_statuses = Vector{Cint}(undef, nconstr)
+        fill!(constr_basis_statuses, -1)  # Default to GRB_NONBASIC
+        
+        for constr_idx in basis.constr_basic
+            constr_basis_statuses[constr_idx] = 0  # GRB_BASIC
+        end
+        
+        ret = GRBsetintattrarray(gurobi_model, "CBasis", 0, nconstr, constr_basis_statuses)
+        if ret != 0
+            error("Failed to set constraint basis statuses in Gurobi: error code $ret")
+        end
+    end
+
+    return nothing
+end
+
+function get_basis_gurobi(model::JuMP.Model)::Basis
+    @timeit "Initialization" begin
+        var_basic = Int[]
+        var_nonbasic = Int[]
+        constr_basic = Int[]
+        constr_nonbasic = Int[]
+        nvars = num_variables(model)
+        sizehint!(var_basic, nvars)
+        sizehint!(var_nonbasic, nvars)
+        gurobi_model = unsafe_backend(model).inner
+    end
+
+    @timeit "Query variables" begin
+        var_basis_statuses = Vector{Cint}(undef, nvars)
+        ret = GRBgetintattrarray(gurobi_model, "VBasis", 0, nvars, var_basis_statuses)
+        if ret != 0
+            error("Failed to get variable basis statuses from Gurobi: error code $ret")
+        end
+        for i in 1:nvars
+            if var_basis_statuses[i] == 0  # GRB_BASIC
+                push!(var_basic, i)
+            elseif var_basis_statuses[i] == -1  # GRB_NONBASIC_LOWER
+                push!(var_nonbasic, i)
+            else
+                error("Unknown variable basis status: $(var_basis_statuses[i])")
+            end
+        end
+    end
+
+    @timeit "Query constraints" begin
+        nconstr = num_constraints(model, AffExpr, MOI.EqualTo{Float64})
+        constr_basis_statuses = Vector{Cint}(undef, nconstr)
+        ret = GRBgetintattrarray(gurobi_model, "CBasis", 0, nconstr, constr_basis_statuses)
+        if ret != 0
+            error("Failed to get constraint basis statuses from Gurobi: error code $ret")
+        end
+        for i in 1:nconstr
+            if constr_basis_statuses[i] == 0  # GRB_BASIC
+                push!(constr_basic, i)
+            elseif constr_basis_statuses[i] == -1  # GRB_NONBASIC
+                push!(constr_nonbasic, i)
+            else
+                error("Unknown constraint basis status: $(constr_basis_statuses[i])")
+            end
+        end
+    end
+
+    @timeit "Build basis struct" begin
+        basis = Basis(; var_basic, var_nonbasic, constr_basic, constr_nonbasic)
+    end
+
+    return basis
 end
 
 function get_x(model::JuMP.Model)
@@ -58,7 +171,12 @@ function compute_tableau(
     x::Union{Nothing,Vector{Float64}} = nothing,
     rows::Union{Vector{Int},Nothing} = nothing,
     tol = 1e-8,
+    estimated_density = 0.10,
 )::Tableau
+    if isnan(estimated_density) || estimated_density <= 0
+        estimated_density = 0.10
+    end
+    
     @timeit "Split data" begin
         nrows, ncols = size(data.constr_lhs)
         lhs_slacks = sparse(I, nrows, nrows)
@@ -73,35 +191,71 @@ function compute_tableau(
         factor = klu(sparse(lhs_b'))
     end
 
-    @timeit "Compute tableau" begin
+    @timeit "Initialize arrays" begin
-        @timeit "Initialize" begin
+        num_rows = length(rows)
-            tableau_rhs = zeros(length(rows))
+        tableau_rhs::Array{Float64} = zeros(num_rows)
-            tableau_lhs = zeros(length(rows), ncols)
+        tableau_rowptr::Array{Int} = zeros(Int, num_rows + 1)
-        end
+        tableau_colval::Array{Int} = Int[]
-        for k in eachindex(1:length(rows))
+        tableau_nzval::Array{Float64} = Float64[]
-            @timeit "Prepare inputs" begin
+        estimated_nnz::Int = round(num_rows * ncols * estimated_density)
-                i = rows[k]
+        sizehint!(tableau_colval, estimated_nnz)
-                e = zeros(nrows)
+        sizehint!(tableau_nzval, estimated_nnz)
-                e[i] = 1.0
+        e::Array{Float64} = zeros(nrows)
+        sol::Array{Float64} = zeros(nrows)
+        tableau_row::Array{Float64} = zeros(ncols)
     end
+
+    A = data.constr_lhs'
+    b = data.constr_ub
+    tableau_rowptr[1] = 1
+
+    @timeit "Process rows" begin
+        for k in eachindex(rows)
             @timeit "Solve" begin
-                sol = factor \ e
+                fill!(e, 0.0)
+                e[rows[k]] = 1.0
+                ldiv!(sol, factor, e)
             end
-            @timeit "Multiply" begin
+            @timeit "Compute row" begin
-                tableau_lhs[k, :] = sol' * data.constr_lhs
+                mul!(tableau_row, A, sol)
-                tableau_rhs[k] = sol' * data.constr_ub
+                tableau_rhs[k] = dot(sol, b)
+            end
+            needed_space = length(tableau_colval) + ncols
+            if needed_space > estimated_nnz
+                @timeit "Grow arrays" begin
+                    estimated_nnz *= 2
+                    sizehint!(tableau_colval, estimated_nnz)
+                    sizehint!(tableau_nzval, estimated_nnz)
                 end
             end
-        @timeit "Sparsify" begin
+            @timeit "Collect nonzeros for row" begin
-            tableau_lhs[abs.(tableau_lhs) .<= tol] .= 0
+                for j in 1:ncols
-            tableau_lhs = sparse(tableau_lhs)
+                    val = tableau_row[j]
+                    if abs(val) > tol
+                        push!(tableau_colval, j)
+                        push!(tableau_nzval, val)
                     end
                 end
+            end
+            tableau_rowptr[k + 1] = length(tableau_colval) + 1
+        end
+    end
+
+    @timeit "Shrink arrays" begin
+        sizehint!(tableau_colval, length(tableau_colval))
+        sizehint!(tableau_nzval, length(tableau_nzval))
+    end
+
+    @timeit "Build sparse matrix" begin
+        tableau_lhs_transposed = SparseMatrixCSC(ncols, num_rows, tableau_rowptr, tableau_colval, tableau_nzval)
+        tableau_lhs = transpose(tableau_lhs_transposed)
+    end
 
     @timeit "Compute tableau objective row" begin
         sol = factor \ obj_b
         tableau_obj = -data.obj' + sol' * data.constr_lhs
         tableau_obj[abs.(tableau_obj).<tol] .= 0
+        tableau_obj = Array(tableau_obj')
     end
 
     # Compute z if solution is provided
@@ -113,4 +267,4 @@ function compute_tableau(
     return Tableau(obj = tableau_obj, lhs = tableau_lhs, rhs = tableau_rhs, z = z)
 end
 
-export get_basis, get_x, compute_tableau
+export get_basis, get_basis_gurobi, set_basis, get_x, compute_tableau

src/Cuts/tableau/transform.jl

@@ -96,46 +96,70 @@ Base.@kwdef mutable struct AddSlackVariables <: Transform
 end
 
 function forward!(t::AddSlackVariables, data::ProblemData)
+    @timeit "Identify constraint type" begin
         nrows, ncols = size(data.constr_lhs)
         isequality = abs.(data.constr_ub .- data.constr_lb) .< 1e-6
         eq = [i for i = 1:nrows if isequality[i]]
         ge = [i for i = 1:nrows if isfinite(data.constr_lb[i]) && !isequality[i]]
         le = [i for i = 1:nrows if isfinite(data.constr_ub[i]) && !isequality[i]]
         EQ, GE, LE = length(eq), length(ge), length(le)
-
+    end
+    @timeit "Identify slack type" begin
+        constr_lhs_t = sparse(data.constr_lhs')
+        function is_integral(row_idx, rhs)
+            rhs_is_integer = abs(rhs - round(rhs)) <= 1e-6
+            cols, coeffs = findnz(constr_lhs_t[:, row_idx])[1:2]
+            vars_are_integer = all(j -> data.var_types[j] ∈ ['I', 'B'], cols)
+            coeffs_are_integer = all(v -> abs(v - round(v)) <= 1e-6, coeffs)
+            return rhs_is_integer && vars_are_integer && coeffs_are_integer
+        end
+        slack_types = [
+            [is_integral(ge[i], data.constr_lb[ge[i]]) ? 'I' : 'C' for i = 1:GE];
+            [is_integral(le[i], data.constr_ub[le[i]]) ? 'I' : 'C' for i = 1:LE]
+        ]
+    end
+    @timeit "Build M1" begin
         t.M1 = [
             I spzeros(ncols, GE + LE)
             data.constr_lhs[ge, :] spzeros(GE, GE + LE)
             -data.constr_lhs[le, :] spzeros(LE, GE + LE)
         ]
+    end
+    @timeit "Build M2" begin
         t.M2 = [
             zeros(ncols)
             data.constr_lb[ge]
             -data.constr_ub[le]
         ]
+    end
+    @timeit "Build t.lhs, t.rhs" begin
         t.ncols_orig = ncols
         t.GE, t.LE = GE, LE
         t.lhs_ge = data.constr_lhs[ge, :]
         t.lhs_le = data.constr_lhs[le, :]
         t.rhs_ge = data.constr_lb[ge]
         t.rhs_le = data.constr_ub[le]
-
+    end
+    @timeit "Build data.constr_lhs" begin
         data.constr_lhs = [
             data.constr_lhs[eq, :] spzeros(EQ, GE) spzeros(EQ, LE)
             data.constr_lhs[ge, :] -I spzeros(GE, LE)
             data.constr_lhs[le, :] spzeros(LE, GE) I
         ]
+    end
+    @timeit "Build other data fields" begin
         data.obj = [data.obj; zeros(GE + LE)]
         data.var_lb = [data.var_lb; zeros(GE + LE)]
         data.var_ub = [data.var_ub; [Inf for _ = 1:(GE+LE)]]
         data.var_names = [data.var_names; ["__s$i" for i = 1:(GE+LE)]]
-    data.var_types = [data.var_types; ['C' for _ = 1:(GE+LE)]]
+        data.var_types = [data.var_types; slack_types]
         data.constr_lb = [
             data.constr_lb[eq]
             data.constr_lb[ge]
             data.constr_ub[le]
         ]
         data.constr_ub = copy(data.constr_lb)
+    end
 end
 
 function backwards!(t::AddSlackVariables, c::ConstraintSet)
@@ -155,71 +179,55 @@ end
 # -----------------------------------------------------------------------------
 
 Base.@kwdef mutable struct SplitFreeVars <: Transform
-    F::Int = 0
+    ncols::Int = 0
-    B::Int = 0
+    is_var_free::Vector{Bool} = []
-    free::Vector{Int} = []
-    others::Vector{Int} = []
 end
 
 function forward!(t::SplitFreeVars, data::ProblemData)
     lhs = data.constr_lhs
     _, ncols = size(lhs)
-    free = [i for i = 1:ncols if !isfinite(data.var_lb[i]) && !isfinite(data.var_ub[i])]
+    is_var_free = [!isfinite(data.var_lb[i]) && !isfinite(data.var_ub[i]) for i = 1:ncols]
-    others = [i for i = 1:ncols if isfinite(data.var_lb[i]) || isfinite(data.var_ub[i])]
+    free_idx = findall(is_var_free)
-    t.F = length(free)
-    t.B = length(others)
-    t.free, t.others = free, others
     data.obj = [
-        data.obj[others]
+        data.obj
-        data.obj[free]
+        [-data.obj[i] for i in free_idx]
-        -data.obj[free]
     ]
-    data.constr_lhs = [lhs[:, others] lhs[:, free] -lhs[:, free]]
     data.var_lb = [
-        data.var_lb[others]
+        [is_var_free[i] ? 0.0 : data.var_lb[i] for i in 1:ncols]
-        [0.0 for _ in free]
+        [0 for _ in free_idx]
-        [0.0 for _ in free]
     ]
     data.var_ub = [
-        data.var_ub[others]
+        [is_var_free[i] ? Inf : data.var_ub[i] for i in 1:ncols]
-        [Inf for _ in free]
+        [Inf for _ in free_idx]
-        [Inf for _ in free]
     ]
     data.var_types = [
-        data.var_types[others]
+        data.var_types
-        data.var_types[free]
+        [data.var_types[i] for i in free_idx]
-        data.var_types[free]
     ]
     data.var_names = [
-        data.var_names[others]
+        data.var_names
-        ["$(v)_p" for v in data.var_names[free]]
+        ["$(data.var_names[i])_neg" for i in free_idx]
-        ["$(v)_m" for v in data.var_names[free]]
     ]
+    data.constr_lhs = [lhs -lhs[:, free_idx]]
+    t.is_var_free, t.ncols = is_var_free, ncols
 end
 
 function backwards!(t::SplitFreeVars, c::ConstraintSet)
-    # Convert GE constraints into LE
+    ncols, is_var_free = t.ncols, t.is_var_free
-    nrows, _ = size(c.lhs)
+    free_idx = findall(is_var_free)
-    ge = [i for i = 1:nrows if isfinite(c.lb[i])]
-    c.ub[ge], c.lb[ge] = -c.lb[ge], -c.ub[ge]
-    c.lhs[ge, :] *= -1
 
-    # Assert only LE constraints are left (EQ constraints are not supported)
+    for (offset, var_idx) in enumerate(free_idx)
-    @assert all(c.lb .== -Inf)
+        @assert c.lhs[:, var_idx] == -c.lhs[:, ncols+offset]
-
-    # Take minimum (weakest) coefficient
-    B, F = t.B, t.F
-    for i = 1:F
-        c.lhs[:, B+i] = min.(c.lhs[:, B+i], -c.lhs[:, B+F+i])
     end
-    c.lhs = c.lhs[:, 1:(B+F)]
+    c.lhs = c.lhs[:, 1:ncols]
 end
 
 function forward(t::SplitFreeVars, p::Vector{Float64})::Vector{Float64}
+    ncols, is_var_free = t.ncols, t.is_var_free
+    free_idx = findall(is_var_free)
     return [
-        p[t.others]
+        [is_var_free[i] ? max(0, p[i]) : p[i] for i in 1:ncols]
-        max.(p[t.free], 0)
+        [max(0, -p[i]) for i in free_idx]
-        max.(-p[t.free], 0)
     ]
 end
 

src/MIPLearn.jl

@@ -13,6 +13,7 @@ include("collectors.jl")
 include("components.jl")
 include("extractors.jl")
 include("io.jl")
+include("problems/maxcut.jl")
 include("problems/setcover.jl")
 include("problems/stab.jl")
 include("problems/tsp.jl")
@@ -24,6 +25,7 @@ function __init__()
     __init_components__()
     __init_extractors__()
     __init_io__()
+    __init_problems_maxcut__()
     __init_problems_setcover__()
     __init_problems_stab__()
     __init_problems_tsp__()
@@ -37,48 +39,48 @@ include("Cuts/Cuts.jl")
 # Precompilation
 # =============================================================================
 
-function __precompile_cuts__()
+# function __precompile_cuts__()
-    function build_model(mps_filename)
+#     function build_model(mps_filename)
-        model = read_from_file(mps_filename)
+#         model = read_from_file(mps_filename)
-        set_optimizer(model, SCIP.Optimizer)
+#         set_optimizer(model, SCIP.Optimizer)
-        return JumpModel(model)
+#         return JumpModel(model)
-    end
+#     end
-    BASEDIR = dirname(@__FILE__)
+#     BASEDIR = dirname(@__FILE__)
-    mps_filename = "$BASEDIR/../test/fixtures/bell5.mps.gz"
+#     mps_filename = "$BASEDIR/../test/fixtures/bell5.mps.gz"
-    h5_filename = "$BASEDIR/../test/fixtures/bell5.h5"
+#     h5_filename = "$BASEDIR/../test/fixtures/bell5.h5"
-    collect_gmi_dual(
+#     collect_gmi_dual(
-        mps_filename;
+#         mps_filename;
-        optimizer=HiGHS.Optimizer,
+#         optimizer=HiGHS.Optimizer,
-        max_rounds = 10,
+#         max_rounds = 10,
-        max_cuts_per_round = 500,
+#         max_cuts_per_round = 500,
-    )
+#     )
-    knn = KnnDualGmiComponent(
+#     knn = KnnDualGmiComponent(
-        extractor = H5FieldsExtractor(instance_fields = ["static_var_obj_coeffs"]),
+#         extractor = H5FieldsExtractor(instance_fields = ["static_var_obj_coeffs"]),
-        k = 2,
+#         k = 2,
-    )
+#     )
-    knn.fit([h5_filename, h5_filename])
+#     knn.fit([h5_filename, h5_filename])
-    solver = LearningSolver(
+#     solver = LearningSolver(
-        components = [
+#         components = [
-            ExpertPrimalComponent(action = SetWarmStart()),
+#             ExpertPrimalComponent(action = SetWarmStart()),
-            knn,
+#             knn,
-        ],
+#         ],
-        skip_lp = true,
+#         skip_lp = true,
-    )
+#     )
-    solver.optimize(mps_filename, build_model)
+#     solver.optimize(mps_filename, build_model)
-end
+# end
 
-@setup_workload begin
+# @setup_workload begin
-    using SCIP
+#     using SCIP
-    using HiGHS
+#     using HiGHS
-    using MIPLearn.Cuts
+#     using MIPLearn.Cuts
-    using PrecompileTools: @setup_workload, @compile_workload
+#     using PrecompileTools: @setup_workload, @compile_workload
 
-    __init__()
+#     __init__()
-    Cuts.__init__()
+#     Cuts.__init__()
 
-    @compile_workload begin
+#     @compile_workload begin
-        __precompile_cuts__()
+#         __precompile_cuts__()
-    end
+#     end
-end
+# end
 
 end # module

src/problems/maxcut.jl

@@ -0,0 +1,31 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020-2025, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+
+using JuMP
+
+global MaxCutData = PyNULL()
+global MaxCutGenerator = PyNULL()
+
+function __init_problems_maxcut__()
+    copy!(MaxCutData, pyimport("miplearn.problems.maxcut").MaxCutData)
+    copy!(MaxCutGenerator, pyimport("miplearn.problems.maxcut").MaxCutGenerator)
+end
+
+function build_maxcut_model_jump(data::Any; optimizer)
+    if data isa String
+        data = read_pkl_gz(data)
+    end
+    nodes = collect(data.graph.nodes())
+    edges = collect(data.graph.edges())
+    model = Model(optimizer)
+    @variable(model, x[nodes], Bin)
+    @objective(
+        model,
+        Min,
+        sum(-data.weights[i] * x[e[1]] * (1 - x[e[2]]) for (i, e) in enumerate(edges))
+    )
+    return JumpModel(model)
+end
+
+export MaxCutData, MaxCutGenerator, build_maxcut_model_jump

src/solvers/jump.jl

@@ -69,14 +69,13 @@ function submit(model::JuMP.Model, constr)
 end
 
 function _extract_after_load(model::JuMP.Model, h5)
-    @info "_extract_after_load"
     if JuMP.objective_sense(model) == MOI.MIN_SENSE
         h5.put_scalar("static_sense", "min")
     else
         h5.put_scalar("static_sense", "max")
     end
-    @time _extract_after_load_vars(model, h5)
+    _extract_after_load_vars(model, h5)
-    @time _extract_after_load_constrs(model, h5)
+    _extract_after_load_constrs(model, h5)
 end
 
 function _extract_after_load_vars(model::JuMP.Model, h5)
@@ -90,14 +89,27 @@ function _extract_after_load_vars(model::JuMP.Model, h5)
         for v in vars
     ]
     types = [JuMP.is_binary(v) ? "B" : JuMP.is_integer(v) ? "I" : "C" for v in vars]
-    obj = objective_function(model, AffExpr)
+
-    obj_coeffs = [v ∈ keys(obj.terms) ? obj.terms[v] : 0.0 for v in vars]
+    # Linear obj terms
+    obj = objective_function(model, QuadExpr)
+    obj_coeffs_linear = [v ∈ keys(obj.aff.terms) ? obj.aff.terms[v] : 0.0 for v in vars]
+
+    # Quadratic obj terms
+    if length(obj.terms) > 0
+        nvars = length(vars)
+        obj_coeffs_quad = zeros(nvars, nvars)
+        for (pair, coeff) in obj.terms
+            obj_coeffs_quad[pair.a.index.value, pair.b.index.value] = coeff
+        end
+        h5.put_array("static_var_obj_coeffs_quad", obj_coeffs_quad)
+    end
+
     h5.put_array("static_var_names", to_str_array(JuMP.name.(vars)))
     h5.put_array("static_var_types", to_str_array(types))
     h5.put_array("static_var_lower_bounds", lb)
     h5.put_array("static_var_upper_bounds", ub)
-    h5.put_array("static_var_obj_coeffs", obj_coeffs)
+    h5.put_array("static_var_obj_coeffs", obj_coeffs_linear)
-    h5.put_scalar("static_obj_offset", obj.constant)
+    h5.put_scalar("static_obj_offset", obj.aff.constant)
 end
 
 function _extract_after_load_constrs(model::JuMP.Model, h5)
@@ -144,7 +156,7 @@ function _extract_after_load_constrs(model::JuMP.Model, h5)
         end
     end
     if isempty(names)
-        error("no model constraints found; note that MIPLearn ignores unnamed constraints")
+        return
     end
     lhs = sparse(lhs_rows, lhs_cols, lhs_values, length(rhs), JuMP.num_variables(model))
     h5.put_sparse("static_constr_lhs", lhs)
@@ -154,11 +166,10 @@ function _extract_after_load_constrs(model::JuMP.Model, h5)
 end
 
 function _extract_after_lp(model::JuMP.Model, h5)
-    @info "_extract_after_lp"
     h5.put_scalar("lp_wallclock_time", solve_time(model))
     h5.put_scalar("lp_obj_value", objective_value(model))
-    @time _extract_after_lp_vars(model, h5)
+    _extract_after_lp_vars(model, h5)
-    @time _extract_after_lp_constrs(model, h5)
+    _extract_after_lp_constrs(model, h5)
 end
 
 function _extract_after_lp_vars(model::JuMP.Model, h5)
@@ -184,46 +195,46 @@ function _extract_after_lp_vars(model::JuMP.Model, h5)
     end
     h5.put_array("lp_var_basis_status", to_str_array(basis_status))
 
-    # # Sensitivity analysis
+    # Sensitivity analysis
-    # obj_coeffs = h5.get_array("static_var_obj_coeffs")
+    obj_coeffs = h5.get_array("static_var_obj_coeffs")
-    # sensitivity_report = lp_sensitivity_report(model)
+    sensitivity_report = lp_sensitivity_report(model)
-    # sa_obj_down, sa_obj_up = Float64[], Float64[]
+    sa_obj_down, sa_obj_up = Float64[], Float64[]
-    # sa_lb_down, sa_lb_up = Float64[], Float64[]
+    sa_lb_down, sa_lb_up = Float64[], Float64[]
-    # sa_ub_down, sa_ub_up = Float64[], Float64[]
+    sa_ub_down, sa_ub_up = Float64[], Float64[]
-    # for (i, v) in enumerate(vars)
+    for (i, v) in enumerate(vars)
-    #     # Objective function
+        # Objective function
-    #     (delta_down, delta_up) = sensitivity_report[v]
+        (delta_down, delta_up) = sensitivity_report[v]
-    #     push!(sa_obj_down, delta_down + obj_coeffs[i])
+        push!(sa_obj_down, delta_down + obj_coeffs[i])
-    #     push!(sa_obj_up, delta_up + obj_coeffs[i])
+        push!(sa_obj_up, delta_up + obj_coeffs[i])
 
-    #     # Lower bound
+        # Lower bound
-    #     if has_lower_bound(v)
+        if has_lower_bound(v)
-    #         constr = LowerBoundRef(v)
+            constr = LowerBoundRef(v)
-    #         (delta_down, delta_up) = sensitivity_report[constr]
+            (delta_down, delta_up) = sensitivity_report[constr]
-    #         push!(sa_lb_down, lower_bound(v) + delta_down)
+            push!(sa_lb_down, lower_bound(v) + delta_down)
-    #         push!(sa_lb_up, lower_bound(v) + delta_up)
+            push!(sa_lb_up, lower_bound(v) + delta_up)
-    #     else
+        else
-    #         push!(sa_lb_down, -Inf)
+            push!(sa_lb_down, -Inf)
-    #         push!(sa_lb_up, -Inf)
+            push!(sa_lb_up, -Inf)
-    #     end
+        end
 
-    #     # Upper bound
+        # Upper bound
-    #     if has_upper_bound(v)
+        if has_upper_bound(v)
-    #         constr = JuMP.UpperBoundRef(v)
+            constr = JuMP.UpperBoundRef(v)
-    #         (delta_down, delta_up) = sensitivity_report[constr]
+            (delta_down, delta_up) = sensitivity_report[constr]
-    #         push!(sa_ub_down, upper_bound(v) + delta_down)
+            push!(sa_ub_down, upper_bound(v) + delta_down)
-    #         push!(sa_ub_up, upper_bound(v) + delta_up)
+            push!(sa_ub_up, upper_bound(v) + delta_up)
-    #     else
+        else
-    #         push!(sa_ub_down, Inf)
+            push!(sa_ub_down, Inf)
-    #         push!(sa_ub_up, Inf)
+            push!(sa_ub_up, Inf)
-    #     end
+        end
-    # end
+    end
-    # h5.put_array("lp_var_sa_obj_up", sa_obj_up)
+    h5.put_array("lp_var_sa_obj_up", sa_obj_up)
-    # h5.put_array("lp_var_sa_obj_down", sa_obj_down)
+    h5.put_array("lp_var_sa_obj_down", sa_obj_down)
-    # h5.put_array("lp_var_sa_ub_up", sa_ub_up)
+    h5.put_array("lp_var_sa_ub_up", sa_ub_up)
-    # h5.put_array("lp_var_sa_ub_down", sa_ub_down)
+    h5.put_array("lp_var_sa_ub_down", sa_ub_down)
-    # h5.put_array("lp_var_sa_lb_up", sa_lb_up)
+    h5.put_array("lp_var_sa_lb_up", sa_lb_up)
-    # h5.put_array("lp_var_sa_lb_down", sa_lb_down)
+    h5.put_array("lp_var_sa_lb_down", sa_lb_down)
 end
 
 
@@ -239,7 +250,7 @@ function _extract_after_lp_constrs(model::JuMP.Model, h5)
     duals = Float64[]
     basis_status = []
     constr_idx = 1
-    # sensitivity_report = lp_sensitivity_report(model)
+    sensitivity_report = lp_sensitivity_report(model)
     for (ftype, stype) in JuMP.list_of_constraint_types(model)
         for constr in JuMP.all_constraints(model, ftype, stype)
             length(JuMP.name(constr)) > 0 || continue
@@ -257,22 +268,21 @@ function _extract_after_lp_constrs(model::JuMP.Model, h5)
                 error("Unknown basis status: $b")
             end
 
-            # # Sensitivity analysis
+            # Sensitivity analysis
-            # (delta_down, delta_up) = sensitivity_report[constr]
+            (delta_down, delta_up) = sensitivity_report[constr]
-            # push!(sa_rhs_down, rhs[constr_idx] + delta_down)
+            push!(sa_rhs_down, rhs[constr_idx] + delta_down)
-            # push!(sa_rhs_up, rhs[constr_idx] + delta_up)
+            push!(sa_rhs_up, rhs[constr_idx] + delta_up)
 
             constr_idx += 1
         end
     end
     h5.put_array("lp_constr_dual_values", duals)
     h5.put_array("lp_constr_basis_status", to_str_array(basis_status))
-    # h5.put_array("lp_constr_sa_rhs_up", sa_rhs_up)
+    h5.put_array("lp_constr_sa_rhs_up", sa_rhs_up)
-    # h5.put_array("lp_constr_sa_rhs_down", sa_rhs_down)
+    h5.put_array("lp_constr_sa_rhs_down", sa_rhs_down)
 end
 
 function _extract_after_mip(model::JuMP.Model, h5)
-    @info "_extract_after_mip"
     h5.put_scalar("mip_obj_value", objective_value(model))
     h5.put_scalar("mip_obj_bound", objective_bound(model))
     h5.put_scalar("mip_wallclock_time", solve_time(model))
@@ -285,9 +295,11 @@ function _extract_after_mip(model::JuMP.Model, h5)
 
     # Slacks
     lhs = h5.get_sparse("static_constr_lhs")
+    if lhs !== nothing
         rhs = h5.get_array("static_constr_rhs")
         slacks = abs.(lhs * x - rhs)
         h5.put_array("mip_constr_slacks", slacks)
+    end
 
     # Cuts and lazy constraints
     ext = model.ext[:miplearn]
@@ -298,9 +310,7 @@ end
 function _fix_variables(model::JuMP.Model, var_names, var_values, stats)
     vars = [variable_by_name(model, v) for v in var_names]
     for (i, var) in enumerate(vars)
-        if isfinite(var_values[i])
+        fix(var, var_values[i], force = true)
-            fix(var, var_values[i], force=true)
-        end
     end
 end
 
@@ -417,7 +427,7 @@ function __init_solvers_jump__()
             constrs_lhs,
             constrs_sense,
             constrs_rhs,
-            stats=nothing,
+            stats = nothing,
         ) = _add_constrs(
             self.inner,
             from_str_array(var_names),
@@ -433,14 +443,14 @@ function __init_solvers_jump__()
 
         extract_after_mip(self, h5) = _extract_after_mip(self.inner, h5)
 
-        fix_variables(self, var_names, var_values, stats=nothing) =
+        fix_variables(self, var_names, var_values, stats = nothing) =
             _fix_variables(self.inner, from_str_array(var_names), var_values, stats)
 
         optimize(self) = _optimize(self.inner)
 
         relax(self) = Class(_relax(self.inner))
 
-        set_warm_starts(self, var_names, var_values, stats=nothing) =
+        set_warm_starts(self, var_names, var_values, stats = nothing) =
             _set_warm_starts(self.inner, from_str_array(var_names), var_values, stats)
 
         write(self, filename) = _write(self.inner, filename)

test/Project.toml

@@ -4,9 +4,7 @@ authors = ["Alinson S. Xavier <git@axavier.org>"]
 version = "0.1.0"
 
 [deps]
-CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
 Clp = "e2554f3b-3117-50c0-817c-e040a3ddf72d"
-DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 GLPK = "60bf3e95-4087-53dc-ae20-288a0d20c6a6"
 Glob = "c27321d9-0574-5035-807b-f59d2c89b15c"
 HDF5 = "f67ccb44-e63f-5c2f-98bd-6dc0ccc4ba2f"

test/src/BB/test_bb.jl

@@ -10,12 +10,9 @@ using Test
 using MIPLearn.BB
 using MIPLearn
 
-using CSV
-using DataFrames
-
 basepath = @__DIR__
 
-function bb_run(optimizer_name, optimizer; large=true)
+function bb_run(optimizer_name, optimizer; large = true)
     @testset "Solve ($optimizer_name)" begin
         @testset "interface" begin
             filename = "$FIXTURES/danoint.mps.gz"
@@ -28,7 +25,7 @@ function bb_run(optimizer_name, optimizer; large=true)
 
             status, obj = BB.solve_relaxation!(mip)
             @test status == :Optimal
-            @test round(obj, digits=6) == 62.637280
+            @test round(obj, digits = 6) == 62.637280
 
             @test BB.name(mip, mip.int_vars[1]) == "xab"
             @test BB.name(mip, mip.int_vars[2]) == "xac"
@@ -38,26 +35,26 @@ function bb_run(optimizer_name, optimizer; large=true)
             @test mip.int_vars_ub[1] == 1.0
 
             vals = BB.values(mip, mip.int_vars)
-            @test round(vals[1], digits=6) == 0.046933
+            @test round(vals[1], digits = 6) == 0.046933
-            @test round(vals[2], digits=6) == 0.000841
+            @test round(vals[2], digits = 6) == 0.000841
-            @test round(vals[3], digits=6) == 0.248696
+            @test round(vals[3], digits = 6) == 0.248696
 
             # Probe (up and down are feasible)
             probe_up, probe_down = BB.probe(mip, mip.int_vars[1], 0.5, 0.0, 1.0, 1_000_000)
-            @test round(probe_down, digits=6) == 62.690000
+            @test round(probe_down, digits = 6) == 62.690000
-            @test round(probe_up, digits=6) == 62.714100
+            @test round(probe_up, digits = 6) == 62.714100
 
             # Fix one variable to zero
             BB.set_bounds!(mip, mip.int_vars[1:1], [0.0], [0.0])
             status, obj = BB.solve_relaxation!(mip)
             @test status == :Optimal
-            @test round(obj, digits=6) == 62.690000
+            @test round(obj, digits = 6) == 62.690000
 
             # Fix one variable to one and another variable variable to zero
             BB.set_bounds!(mip, mip.int_vars[1:2], [1.0, 0.0], [1.0, 0.0])
             status, obj = BB.solve_relaxation!(mip)
             @test status == :Optimal
-            @test round(obj, digits=6) == 62.714777
+            @test round(obj, digits = 6) == 62.714777
 
             # Fix all binary variables to one, making problem infeasible
             N = length(mip.int_vars)
@@ -71,7 +68,7 @@ function bb_run(optimizer_name, optimizer; large=true)
             BB.set_bounds!(mip, mip.int_vars, zeros(N), ones(N))
             status, obj = BB.solve_relaxation!(mip)
             @test status == :Optimal
-            @test round(obj, digits=6) == 62.637280
+            @test round(obj, digits = 6) == 62.637280
         end
 
         @testset "varbranch" begin
@@ -85,8 +82,8 @@ function bb_run(optimizer_name, optimizer; large=true)
                     BB.StrongBranching(),
                     BB.ReliabilityBranching(),
                     BB.HybridBranching(),
-                    BB.StrongBranching(aggregation=:min),
+                    BB.StrongBranching(aggregation = :min),
-                    BB.ReliabilityBranching(aggregation=:min, collect=true),
+                    BB.ReliabilityBranching(aggregation = :min, collect = true),
                 ]
                     h5 = H5File("$FIXTURES/$instance.h5")
                     mip_obj_bound = h5.get_scalar("mip_obj_bound")
@@ -107,13 +104,13 @@ function bb_run(optimizer_name, optimizer; large=true)
         end
 
         @testset "collect" begin
-            rule = BB.ReliabilityBranching(collect=true)
+            rule = BB.ReliabilityBranching(collect = true)
             BB.collect!(
                 optimizer,
                 "$FIXTURES/bell5.mps.gz",
-                node_limit=100,
+                node_limit = 100,
-                print_interval=10,
+                print_interval = 10,
-                branch_rule=rule,
+                branch_rule = rule,
             )
             n_sb = rule.stats.num_strong_branch_calls
             h5 = H5File("$FIXTURES/bell5.h5")
@@ -131,67 +128,3 @@ function test_bb()
     @time bb_run("HiGHS", optimizer_with_attributes(HiGHS.Optimizer))
     # @time bb_run("CPLEX", optimizer_with_attributes(CPLEX.Optimizer, "CPXPARAM_Threads" => 1))
 end
-
-function test_bb_replay()
-    rule_sb = BB.StrongBranching()
-    rule_rb = BB.ReliabilityBranching()
-    optimizer = optimizer_with_attributes(HiGHS.Optimizer)
-    filenames = [replace(f, ".h5" => "") for f in glob("test/fixtures/stab/*.h5")]
-    results_filename = "tmp.csv"
-
-    lk = ReentrantLock()
-    results = []
-
-    function push_result(r)
-        lock(lk) do
-            push!(results, r)
-            df = DataFrame()
-            for row in results
-                push!(df, row, cols=:union)
-            end
-            CSV.write(results_filename, df)
-        end
-    end
-
-    function solve(filename; replay=nothing, skip=false, rule)
-        has_replay = (replay !== nothing)
-        h5 = H5File("$filename.h5", "r")
-        mip_obj_bound = h5.get_scalar("mip_obj_bound")
-        @show filename
-        @show has_replay
-        h5.file.close()
-        mip = BB.init(optimizer)
-        BB.read!(mip, "$filename.mps.gz")
-        time_solve = @elapsed begin
-            pool, replay = BB.solve!(
-                mip,
-                initial_primal_bound=mip_obj_bound,
-                print_interval=100,
-                node_limit=1_000,
-                branch_rule=rule,
-                replay=replay,
-            )
-        end
-        if !skip
-            push_result(
-                Dict(
-                    "Filename" => filename,
-                    "Replay?" => has_replay,
-                    "Solve time (s)" => time_solve,
-                    "Relative MIP gap (%)" => round(pool.gap * 100, digits=3)
-                )
-            )
-        end
-        return replay
-    end
-
-    # Solve reference instance
-    replay = solve(filenames[1], skip=true, rule=rule_sb)
-
-    # Solve perturbations
-    for i in 2:6
-        solve(filenames[i], rule=rule_rb, replay=nothing)
-        solve(filenames[i], rule=rule_rb, replay=deepcopy(replay))
-    end
-    return
-end

test/src/MIPLearnT.jl

@@ -24,6 +24,7 @@ include("Cuts/tableau/test_gmi_dual.jl")
 include("problems/test_setcover.jl")
 include("problems/test_stab.jl")
 include("problems/test_tsp.jl")
+include("problems/test_maxcut.jl")
 include("solvers/test_jump.jl")
 include("test_io.jl")
 include("test_usage.jl")
@@ -37,6 +38,7 @@ function runtests()
         test_problems_setcover()
         test_problems_stab()
         test_problems_tsp()
+        test_problems_maxcut()
         test_solvers_jump()
         test_usage()
         test_cuts()

test/src/problems/test_maxcut.jl

@@ -0,0 +1,54 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020-2025, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+
+using PyCall
+
+function test_problems_maxcut()
+    np = pyimport("numpy")
+    random = pyimport("random")
+    scipy_stats = pyimport("scipy.stats")
+    randint = scipy_stats.randint
+    uniform = scipy_stats.uniform
+
+    # Set random seed
+    random.seed(42)
+    np.random.seed(42)
+
+    # Build random instance
+    data = MaxCutGenerator(
+        n = randint(low = 10, high = 11),
+        p = uniform(loc = 0.5, scale = 0.0),
+        fix_graph = false,
+    ).generate(
+        1,
+    )[1]
+
+    # Build model
+    model = build_maxcut_model_jump(data, optimizer = SCIP.Optimizer)
+
+    # Check static features
+    h5 = H5File(tempname(), "w")
+    model.extract_after_load(h5)
+    obj_linear = h5.get_array("static_var_obj_coeffs")
+    obj_quad = h5.get_array("static_var_obj_coeffs_quad")
+    @test obj_linear == [3.0, 1.0, 3.0, 1.0, -1.0, 0.0, -1.0, 0.0, -1.0, 0.0]
+    @test obj_quad == [
+        0.0 0.0 -1.0 1.0 -1.0 0.0 0.0 0.0 -1.0 -1.0
+        0.0 0.0 1.0 -1.0 0.0 -1.0 -1.0 0.0 0.0 1.0
+        0.0 0.0 0.0 0.0 0.0 -1.0 0.0 0.0 -1.0 -1.0
+        0.0 0.0 0.0 0.0 0.0 -1.0 1.0 -1.0 0.0 0.0
+        0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0
+        0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
+        0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0
+        0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -1.0
+        0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0
+        0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
+    ]
+
+    # Check optimal solution
+    model.optimize()
+    model.extract_after_mip(h5)
+    @test h5.get_scalar("mip_obj_value") == -4
+    h5.close()
+end