Solvers

13. Solvers#

13.1. miplearn.solvers.abstract#

class miplearn.solvers.abstract.AbstractModel#

Bases: ABC

WHERE_CUTS = 'cuts'#
WHERE_DEFAULT = 'default'#
WHERE_LAZY = 'lazy'#
abstract add_constrs(var_names: ndarray, constrs_lhs: ndarray, constrs_sense: ndarray, constrs_rhs: ndarray, stats: Dict | None = None) → None#
abstract extract_after_load(h5: H5File) → None#
abstract extract_after_lp(h5: H5File) → None#
abstract extract_after_mip(h5: H5File) → None#
abstract fix_variables(var_names: ndarray, var_values: ndarray, stats: Dict | None = None) → None#
lazy_enforce(violations: List[Any]) → None#
abstract optimize() → None#
abstract relax() → AbstractModel#
set_cuts(cuts: List) → None#
abstract set_warm_starts(var_names: ndarray, var_values: ndarray, stats: Dict | None = None) → None#
abstract write(filename: str) → None#

13.2. miplearn.solvers.gurobi#

class miplearn.solvers.gurobi.GurobiModel(inner: Model, lazy_separate: Callable | None = None, lazy_enforce: Callable | None = None, cuts_separate: Callable | None = None, cuts_enforce: Callable | None = None)#

Bases: AbstractModel

add_constr(constr: Any) → None#
add_constrs(var_names: ndarray, constrs_lhs: ndarray, constrs_sense: ndarray, constrs_rhs: ndarray, stats: Dict | None = None) → None#
extract_after_load(h5: H5File) → None#

Given a model that has just been loaded, extracts static problem features, such as variable names and types, objective coefficients, etc.

extract_after_lp(h5: H5File) → None#

Given a linear programming model that has just been solved, extracts dynamic problem features, such as optimal LP solution, basis status, etc.

extract_after_mip(h5: H5File) → None#

Given a mixed-integer linear programming model that has just been solved, extracts dynamic problem features, such as optimal MIP solution.

fix_variables(var_names: ndarray, var_values: ndarray, stats: Dict | None = None) → None#
optimize() → None#
relax() → GurobiModel#
set_time_limit(time_limit_sec: float) → None#
set_warm_starts(var_names: ndarray, var_values: ndarray, stats: Dict | None = None) → None#
write(filename: str) → None#

13.3. miplearn.solvers.learning#

class miplearn.solvers.learning.LearningSolver(components: List[Any], skip_lp: bool = False)#

Bases: object

fit(data_filenames: List[str]) → None#
optimize(model: str | AbstractModel, build_model: Callable | None = None) → Tuple[AbstractModel, Dict[str, Any]]#
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