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Remove unnecessary Alp module reference #247

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merged 3 commits into from
Aug 12, 2024
Merged

Remove unnecessary Alp module reference #247

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e402b95
Remove unnecessary Alp module reference
odow Aug 11, 2024
5731ab5
Merge branch 'master' into od/alp
odow Aug 12, 2024
18a4adb
Update src/log.jl
odow Aug 12, 2024
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1 change: 0 additions & 1 deletion src/Alpine.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -7,7 +7,6 @@ import LinearAlgebra: dot, Diagonal
import Statistics

const ALPINE_DEBUG = false
const Alp = Alpine

include("const.jl")

Expand Down
12 changes: 6 additions & 6 deletions src/MOI_wrapper/MOI_wrapper.jl
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Expand Up @@ -96,7 +96,7 @@ mutable struct Optimizer <: MOI.AbstractOptimizer
bound_sol_pool::Dict{Any,Any} # A pool of solutions from solving model_mip

# Linking constraints info for Multilinear terms
linking_constraints_info::Union{Nothing,Dict{Any,Any}} # Stored multilinear linking constraints info
linking_constraints_info::Union{Nothing,Dict{Any,Any}} # Stored multilinear linking constraints info

# Logging information and status
logs::Dict{Symbol,Any} # Logging information
Expand All @@ -108,7 +108,7 @@ mutable struct Optimizer <: MOI.AbstractOptimizer
# Constructor for Alpine.Optimizer
function Optimizer()
m = new()
m.options = Alp.get_default_options()
m.options = get_default_options()
MOI.empty!(m)
return m
end
Expand Down Expand Up @@ -254,10 +254,10 @@ MOI.get(::Optimizer, ::MOI.SolverName) = "Alpine"
MOI.get(::Optimizer, ::MOI.SolverVersion) = _ALPINE_VERSION

function MOI.set(model::Optimizer, param::MOI.RawOptimizerAttribute, value)
return Alp.set_option(model, Symbol(param.name), value)
return set_option(model, Symbol(param.name), value)
end
function MOI.get(model::Optimizer, param::MOI.RawOptimizerAttribute)
return Alp.get_option(model, Symbol(param.name))
return get_option(model, Symbol(param.name))
end

function MOI.add_variables(model::Optimizer, n::Int)
Expand Down Expand Up @@ -395,10 +395,10 @@ is_max_sense(model::Optimizer) = model.sense_orig == MOI.MAX_SENSE

function MOI.set(model::Optimizer, ::MOI.ObjectiveSense, sense)
model.sense_orig = sense
if Alp.is_max_sense(model)
if is_max_sense(model)
model.best_obj = -Inf
model.best_bound = Inf
elseif Alp.is_min_sense(model)
elseif is_min_sense(model)
model.best_obj = Inf
model.best_bound = -Inf
else
Expand Down
125 changes: 61 additions & 64 deletions src/bounding_model.jl
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21 changes: 7 additions & 14 deletions src/embedding.jl
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Expand Up @@ -5,14 +5,14 @@
function embedding_map(λCnt::Int, encoding::Any = ebd_gray, ibs::Bool = false)
map = Dict()

encoding = Alp.resolve_encoding_key(encoding)
encoding = resolve_encoding_key(encoding)
L = Int(ceil(log(2, λCnt - 1)))
for i in 1:L*2
map[i] = Set()
end
H = [encoding(i, L) for i in 0:max(1, (2^L - 1))]
map[:H_orig] = H
map[:H] = [Alp.ebd_support_binary_vec(H[i]) for i in 1:length(H)] # Store the map
map[:H] = [ebd_support_binary_vec(H[i]) for i in 1:length(H)] # Store the map
map[:L] = L

!is_compatible_encoding(H) && error("Encodign method is not SOS-2 compatible...")
Expand Down Expand Up @@ -57,7 +57,7 @@ end
This function is the same σ() function described in Vielma and Nemhauser 2011.
"""
function ebd_σ(b::String)
sv = Alp.ebd_support_bool_vec(b)
sv = ebd_support_bool_vec(b)
return [i for i in 1:length(sv) if sv[i]]
end

Expand All @@ -82,10 +82,7 @@ end
function is_compatible_encoding(code_seq::Vector)
for i in 1:(length(code_seq)-1)
sum(
abs.(
Alp.ebd_support_bool_vec(code_seq[i]) -
Alp.ebd_support_bool_vec(code_seq[i+1])
),
abs.(ebd_support_bool_vec(code_seq[i]) - ebd_support_bool_vec(code_seq[i+1])),
) != 1 && return false
end
return true
Expand Down Expand Up @@ -132,8 +129,8 @@ function ebd_link_xα(

# Expression expansion
for i in 1:P
code_vec = Alp.ebd_support_bool_vec(code_seq[i])
lifters, exprs = Alp.ebd_link_expression(code_vec, lifters, exprs, i)
code_vec = ebd_support_bool_vec(code_seq[i])
lifters, exprs = ebd_link_expression(code_vec, lifters, exprs, i)
end

# Construct Variable Vector
Expand All @@ -145,11 +142,7 @@ function ebd_link_xα(
base_name = "αA$(var_idx)"
)
for i in keys(lifters) # Build first-level evaluation
Alp.relaxation_multilinear_binary(
m.model_mip,
α_A[lifters[i]-L],
[α[j] for j in i],
)
relaxation_multilinear_binary(m.model_mip, α_A[lifters[i]-L], [α[j] for j in i])
end

α_R = [α; α_A] # Initialize/re-arrgange the variable sequence
Expand Down
4 changes: 2 additions & 2 deletions src/heuristics.jl
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Expand Up @@ -21,9 +21,9 @@ function update_disc_cont_var(m::Optimizer)
(var_diffs = _eval_var_diffs!(m.nonconvex_terms, var_diffs))

distance = Dict(zip(var_idxs, var_diffs))
Alp.weighted_min_vertex_cover(m, distance)
weighted_min_vertex_cover(m, distance)

(Alp.get_option(m, :log_level) > 100) &&
(get_option(m, :log_level) > 100) &&
println("updated partition var selection => $(m.disc_vars)")
return
end
Expand Down
77 changes: 35 additions & 42 deletions src/log.jl
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Expand Up @@ -5,7 +5,7 @@ function create_logs!(m)
# Timers
logs[:presolve_time] = 0.0 # Total presolve-time of the algorithm
logs[:total_time] = 0.0 # Total run-time of the algorithm
logs[:time_left] = Alp.get_option(m, :time_limit) # Total remaining time of the algorithm if time-out is specified
logs[:time_left] = get_option(m, :time_limit) # Total remaining time of the algorithm if time-out is specified

# Values
logs[:obj] = [] # Iteration-based objective
Expand All @@ -23,15 +23,15 @@ end

function reset_timer(m::Optimizer)
m.logs[:total_time] = 0.0
m.logs[:time_left] = Alp.get_option(m, :time_limit)
m.logs[:time_left] = get_option(m, :time_limit)
return m
end

function logging_summary(m::Optimizer)
if Alp.get_option(m, :log_level) > 0
if get_option(m, :log_level) > 0
printstyled("\nPROBLEM STATISTICS\n", color = :cyan, bold = true)
Alp.is_min_sense(m) && (println(" Objective sense = Min"))
Alp.is_max_sense(m) && (println(" Objective sense = Max"))
is_min_sense(m) && (println(" Objective sense = Min"))
is_max_sense(m) && (println(" Objective sense = Max"))
println(
" # Variables = ",
length([i for i in 1:m.num_var_orig if m.var_type[i] == :Cont]) +
Expand All @@ -45,7 +45,7 @@ function logging_summary(m::Optimizer)
println(" # Constraints = ", m.num_constr_orig)
println(" # NL Constraints = ", m.num_nlconstr_orig)
println(" # Linear Constraints = ", m.num_lconstr_orig)
Alp.get_option(m, :recognize_convex) && println(
get_option(m, :recognize_convex) && println(
" # Detected convex constraints = $(length([i for i in m.constr_structure if i == :convex]))",
)
println(" # Detected nonlinear terms = ", length(m.nonconvex_terms))
Expand All @@ -56,7 +56,7 @@ function logging_summary(m::Optimizer)
println(" # Potential variables for partitioning = ", length(m.disc_vars))

printstyled("SUB-SOLVERS USED BY ALPINE\n", color = :cyan, bold = true)
if Alp.get_option(m, :minlp_solver) === nothing
if get_option(m, :minlp_solver) === nothing
println(" NLP local solver = ", m.nlp_solver_id)
else
println(" MINLP local solver = ", m.minlp_solver_id)
Expand All @@ -70,63 +70,56 @@ function logging_summary(m::Optimizer)
if Alp.is_min_sense(m)
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println(
" Maximum iterations (lower-bounding MIPs) = ",
Alp.get_option(m, :max_iter),
get_option(m, :max_iter),
)
elseif Alp.is_max_sense(m)
elseif is_max_sense(m)
println(
" Maximum iterations (upper-bounding MIPs) = ",
Alp.get_option(m, :max_iter),
get_option(m, :max_iter),
)
else
println(
" Maximum iterations (bounding MIPs) = ",
Alp.get_option(m, :max_iter),
)
println(" Maximum iterations (bounding MIPs) = ", get_option(m, :max_iter))
end

println(
" Relative global optimality gap = ",
Alp.get_option(m, :rel_gap) * 100,
"%",
)
println(" Relative global optimality gap = ", get_option(m, :rel_gap) * 100, "%")

if Alp.get_option(m, :disc_var_pick) == 0
if get_option(m, :disc_var_pick) == 0
println(" Potential variables chosen for partitioning = All")
elseif Alp.get_option(m, :disc_var_pick) == 1
elseif get_option(m, :disc_var_pick) == 1
println(
" Potential variables chosen for partitioning = Minimum vertex cover",
)
end

if Alp.get_option(m, :partition_scaling_factor_branch)
if get_option(m, :partition_scaling_factor_branch)
println(" Partition scaling factor branch activated")
else
println(
" Partition scaling factor = ",
Alp.get_option(m, :partition_scaling_factor),
get_option(m, :partition_scaling_factor),
)
end
(Alp.get_option(m, :convhull_ebd)) && println(" Using convhull_ebd formulation")
(Alp.get_option(m, :convhull_ebd)) &&
println(" Encoding method = $(Alp.get_option(m, :convhull_ebd_encode))")
(Alp.get_option(m, :convhull_ebd)) && println(
" Independent branching scheme = $(Alp.get_option(m, :convhull_ebd_ibs))",
(get_option(m, :convhull_ebd)) && println(" Using convhull_ebd formulation")
(get_option(m, :convhull_ebd)) &&
println(" Encoding method = $(get_option(m, :convhull_ebd_encode))")
(get_option(m, :convhull_ebd)) && println(
" Independent branching scheme = $(get_option(m, :convhull_ebd_ibs))",
)
println(" Bound-tightening presolve = ", Alp.get_option(m, :presolve_bt))
Alp.get_option(m, :presolve_bt) && println(
println(" Bound-tightening presolve = ", get_option(m, :presolve_bt))
get_option(m, :presolve_bt) && println(
" Maximum iterations (OBBT) = ",
Alp.get_option(m, :presolve_bt_max_iter),
get_option(m, :presolve_bt_max_iter),
)
end
end

function logging_head(m::Optimizer)
if Alp.is_min_sense(m)
if is_min_sense(m)
printstyled("LOWER-BOUNDING ITERATIONS", color = :cyan, bold = true)
UB_iter = "Incumbent"
UB = "Best Incumbent"
LB = "Lower Bound"
elseif Alp.is_max_sense(m)
elseif is_max_sense(m)
printstyled("UPPER-BOUNDING ITERATIONS", color = :cyan, bold = true)
UB_iter = "Incumbent"
UB = "Best Incumbent"
Expand Down Expand Up @@ -244,7 +237,7 @@ function summary_status(m::Optimizer)

if m.detected_bound && m.detected_incumbent
m.alpine_status =
m.best_rel_gap > Alp.get_option(m, :rel_gap) ? MOI.OTHER_LIMIT : MOI.OPTIMAL
m.best_rel_gap > get_option(m, :rel_gap) ? MOI.OTHER_LIMIT : MOI.OPTIMAL
elseif m.status[:bounding_solve] == MOI.INFEASIBLE
m.alpine_status = MOI.INFEASIBLE
elseif m.detected_bound && !m.detected_incumbent
Expand All @@ -267,11 +260,11 @@ end
# cnt = length([1 for j in keys(m.nonconvex_terms) if m.nonconvex_terms[j][:nonlinear_type] == i])
# cnt > 0 && println("\tTerm $(i) Count = $(cnt) ")
# end
# println(" Maximum solution time = ", Alp.get_option(m, :time_limit))
# println(" Basic bound propagation = ", Alp.get_option(m, :presolve_bp))
# println(" Conseuctive solution rejection = after ", Alp.get_option(m, :disc_consecutive_forbid), " times")
# Alp.get_option(m, :presolve_bt) && println("bound tightening presolve algorithm = ", Alp.get_option(m, :presolve_bt)_algo)
# Alp.get_option(m, :presolve_bt) && println("bound tightening presolve width tolerance = ", Alp.get_option(m, :presolve_bt)_width_tol)
# Alp.get_option(m, :presolve_bt) && println("bound tightening presolve output tolerance = ", Alp.get_option(m, :presolve_bt)_output_tol)
# Alp.get_option(m, :presolve_bt) && println("bound tightening presolve relaxation = ", Alp.get_option(m, :presolve_bt)_relax)
# Alp.get_option(m, :presolve_bt) && println("bound tightening presolve mip regulation time = ", Alp.get_option(m, :presolve_bt)_mip_time_limit)
# println(" Maximum solution time = ", get_option(m, :time_limit))
# println(" Basic bound propagation = ", get_option(m, :presolve_bp))
# println(" Conseuctive solution rejection = after ", get_option(m, :disc_consecutive_forbid), " times")
# get_option(m, :presolve_bt) && println("bound tightening presolve algorithm = ", get_option(m, :presolve_bt)_algo)
# get_option(m, :presolve_bt) && println("bound tightening presolve width tolerance = ", get_option(m, :presolve_bt)_width_tol)
# get_option(m, :presolve_bt) && println("bound tightening presolve output tolerance = ", get_option(m, :presolve_bt)_output_tol)
# get_option(m, :presolve_bt) && println("bound tightening presolve relaxation = ", get_option(m, :presolve_bt)_relax)
# get_option(m, :presolve_bt) && println("bound tightening presolve mip regulation time = ", get_option(m, :presolve_bt)_mip_time_limit)
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