def cuts_generator_options():
"""Outer Approximation cuts generator options"""
return CutsGeneratorOptions(
MixedIntegerOuterApproximationCutsGenerator.name, [
NumericOption('threshold', default=1e-3),
NumericOption('convergence_relative_tol', default=1e-3),
])
def bab_options():
return [
NumericOption('absolute_gap', default=1e-8),
NumericOption('relative_gap', default=1e-6),
IntegerOption('node_limit', default=100000000),
IntegerOption('root_node_feasible_solution_seed', default=None),
NumericOption('root_node_feasible_solution_search_timelimit',
default=30),
IntegerOption('fbbt_maxiter', default=10),
IntegerOption('obbt_simplex_maxiter', default=100),
NumericOption('obbt_timelimit', default=60),
NumericOption('fbbt_timelimit', default=10),
IntegerOption('fbbt_max_quadratic_size', default=100),
IntegerOption('fbbt_max_expr_children', default=100),
BoolOption('catch_keyboard_interrupt', default=True),
NumericOption(
'branching_weight_sum',
default=1.0,
description='Weight of the sum of nonlinear infeasibility'),
NumericOption(
'branching_weight_max',
default=0.0,
description='Weight of the max of nonlinear infeasibility'),
NumericOption(
'branching_weight_min',
default=0.0,
description='Weight of the min of nonlinear infeasibility'),
NumericOption(
'branching_weight_lambda',
default=0.25,
description=
'Weight of the midpoint when computing convex combination with solution for branching'
),
]
def algorithm_options():
"""Return options for BranchAndCutAlgorithm"""
return OptionsGroup('cuts', [
IntegerOption(
'maxiter',
default=20,
description='Number of cut rounds'
),
NumericOption(
'cut_tolerance',
default=1e-5,
description='Terminate if two consecutive cut rounds are within this tolerance'
),
IntegerOption(
'timelimit',
default=120,
description='Total timelimit for cut rounds'
),
BoolOption(
'use_lp_cut_phase',
default=True,
description='Solve LP in cut loop'
),
BoolOption(
'use_milp_cut_phase',
default=False,
description='Add additional cut loop solving MILP'
),
NumericOption(
'branching_weight_sum',
default=1.0,
description='Weight of the sum of nonlinear infeasibility'
),
NumericOption(
'branching_weight_max',
default=0.0,
description='Weight of the max of nonlinear infeasibility'
),
NumericOption(
'branching_weight_min',
default=0.0,
description='Weight of the min of nonlinear infeasibility'
),
NumericOption(
'branching_weight_lambda',
default=0.25,
description='Weight of the midpoint when computing convex combination with solution for branching'
),
])
def solver_options():
return SolverOptions(BranchAndBoundSolver.name, [
NumericOption('tolerance', default=1e-6),
NumericOption('relative_tolerance', default=1e-6),
IntegerOption('node_limit', default=100000000),
IntegerOption('root_node_feasible_solution_seed', default=None),
NumericOption('root_node_feasible_solution_search_timelimit',
default=6000000),
IntegerOption('fbbt_maxiter', default=10),
IntegerOption('obbt_simplex_maxiter', default=1000),
NumericOption('obbt_timelimit', default=6000000),
NumericOption('fbbt_timelimit', default=6000000),
IntegerOption('fbbt_max_quadratic_size', default=1000),
IntegerOption('fbbt_max_expr_children', default=1000),
BoolOption('catch_keyboard_interrupt', default=True),
BranchAndCutAlgorithm.algorithm_options(),
])
def cuts_generator_options():
return CutsGeneratorOptions(SdpCutsGenerator.name, [
NumericOption('domain_eps',
default=1e-3,
description="Minimum domain length for each variable to consider cut on"),
NumericOption('selection_size',
default=0.1,
description="Cut selection size as a % of all cuts or as absolute number of cuts"),
NumericOption('thres_sdp_viol',
default=-1e-15,
description="Violation (negative eigenvalue) threshold for separation of SDP cuts"),
NumericOption('min_sdp_cuts_per_round',
default=1e1,
description="Min number of SDP cuts to be added to relaxation at each cut round"),
NumericOption('max_sdp_cuts_per_round',
default=5e3,
description="Max number of SDP cuts to be added to relaxation at each cut round"),
NumericOption('dim',
default=3,
description="Dimension of SDP decomposition/cuts - min=2, max=5"),
EnumOption('cut_sel_strategy',
default="COMB_ONE_CON",
values=["OPT", "FEAS", "RANDOM", "COMB_ONE_CON", "COMB_ALL_CON"]),
NumericOption('big_m',
default=10e3,
description="Big M constant value for combined optimality/feasibility cut selection strategy"),
NumericOption('thres_min_opt_sel',
default=0,
description="Threshold of minimum optimality measure to select cut in "
"combined optimality/feasibility cut selection strategy"),
NumericOption('thres_min_coeff_comb',
default=1e-5,
description="Threshold for the min absolute value of a valid coefficient when combining "
"constraints based on Lagrange multipliers for optimality cut selection"),
NumericOption('convergence_relative_tolerance',
default=1e-3,
description='Termination criteria on lower bound improvement between '
'two consecutive cut rounds <= relative_tolerance % of '
'lower bound improvement from cut round'),
])
def cuts_generator_options():
return CutsGeneratorOptions(TriangleCutsGenerator.name, [
NumericOption(
'domain_eps',
default=1e-3,
description=
'Minimum domain length for each variable to consider cut on'),
NumericOption(
'selection_size',
default=0.1,
description=
'Cut selection size as a % of all cuts or as absolute number of cuts'
),
NumericOption(
'thres_triangle_viol',
default=1e-7,
description=
'Violation threshold for separation of triangle inequalities'),
NumericOption(
'min_tri_cuts_per_round',
default=5e3,
description=
'Min number of triangle cuts to be added to relaxation at each cut round'
),
NumericOption(
'max_tri_cuts_per_round',
default=10e3,
description=
'Max number of triangle cuts to be added to relaxation at each cut round'
),
NumericOption(
'convergence_relative_tolerance',
default=1e-3,
description=
'Termination criteria on lower bound improvement between '
'two consecutive cut rounds <= relative_tolerance % of '
'lower bound improvement from cut round'),
])
def solver_options():
return SolverOptions(AlphaBBSolver.name, [
NumericOption('tolerance', default=1e-8),
NumericOption('relative_tolerance', default=1e-8),
IntegerOption('node_limit', default=100000000),
])
def cuts_generator_options():
"""Quadratic Outer Approximation cuts generator options"""
return CutsGeneratorOptions(OuterApproximationCutsGenerator.name, [
NumericOption('deviation_threshold', default=1e-5),
NumericOption('convergence_relative_tol', default=1e-3),
])