Contents

12. Solvers

12.1. miplearn.solvers.abstract

class miplearn.solvers.abstract.AbstractModel

Bases: abc.ABC

abstract add_constrs(var_names: numpy.ndarray, constrs_lhs: numpy.ndarray, constrs_sense: numpy.ndarray, constrs_rhs: numpy.ndarray, stats: Optional[Dict] = None) → None
abstract extract_after_load(h5: miplearn.h5.H5File) → None
abstract extract_after_lp(h5: miplearn.h5.H5File) → None
abstract extract_after_mip(h5: miplearn.h5.H5File) → None
abstract fix_variables(var_names: numpy.ndarray, var_values: numpy.ndarray, stats: Optional[Dict] = None) → None
abstract optimize() → None
abstract relax() → miplearn.solvers.abstract.AbstractModel
abstract set_warm_starts(var_names: numpy.ndarray, var_values: numpy.ndarray, stats: Optional[Dict] = None) → None
abstract write(filename: str) → None

12.2. miplearn.solvers.gurobi

class miplearn.solvers.gurobi.GurobiModel(inner: gurobipy.Model, find_violations: Optional[Callable] = None, fix_violations: Optional[Callable] = None)

Bases: object

add_constrs(var_names: numpy.ndarray, constrs_lhs: numpy.ndarray, constrs_sense: numpy.ndarray, constrs_rhs: numpy.ndarray, stats: Optional[Dict] = None) → None
extract_after_load(h5: miplearn.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: miplearn.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: miplearn.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: numpy.ndarray, var_values: numpy.ndarray, stats: Optional[Dict] = None) → None
optimize() → None
relax() → miplearn.solvers.gurobi.GurobiModel
set_time_limit(time_limit_sec: float) → None
set_warm_starts(var_names: numpy.ndarray, var_values: numpy.ndarray, stats: Optional[Dict] = None) → None
write(filename: str) → None

12.3. miplearn.solvers.learning

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

Bases: object

fit(data_filenames)
optimize(model: Union[str, miplearn.solvers.abstract.AbstractModel], build_model=None)
11. Components 13. Helpers