jijzept_solver.core

Module Contents

class ALMSimulatedAnnealing
static from_ommx_instance(ommx_instance_buf, id_translator, *, initial_ommx_state_buf=None, num_samples=None, num_iters=4, time_limit_msec_per_iter=None, count_per_iter=None, disable_annealing=False, allow_optimal_move=True, alm_search_option_number=0, normalize_coefficients=True, seed=None, log_cost=None, log_interval_sec=None, global_start_time=None)
Parameters:

  • ommx_instance_buf (bytes)

  • id_translator (IdTranslator)

  • initial_ommx_state_buf (Optional[bytes])

  • num_samples (Optional[int])

  • num_iters (Optional[int])

  • time_limit_msec_per_iter (Optional[int])

  • count_per_iter (Optional[int])

  • disable_annealing (Optional[bool])

  • allow_optimal_move (Optional[bool])

  • alm_search_option_number (Optional[int])

  • normalize_coefficients (Optional[bool])

  • seed (Optional[int])

  • log_cost (Optional[bool])

  • log_interval_sec (Optional[float])

  • global_start_time (Optional[float])

Return type:

ALMSimulatedAnnealing

solve(*, cancel_token=None)
Parameters:

cancel_token (Optional[jijzept_solver.solver_parameters.CancelToken])

Return type:

list[list[tuple[int, float]]]

convergence_history: dict[str, list[float]]
class IdTranslator(ommx_instance_buf)
Parameters:

ommx_instance_buf (bytes)

static from_data(data)
Parameters:

data (tuple[list[int], list[tuple[int, int]], list[tuple[int, int]], list[tuple[int, tuple[float, float]]], int, int, int])

Return type:

IdTranslator

num_used_variables()
Return type:

int

to_data()
Return type:

tuple[list[int], list[tuple[int, int]], list[tuple[int, int]], list[tuple[int, tuple[float, float]]], int, int, int]

property constraint_degree: int
Return type:

int

property objective_degree: int
Return type:

int

property problem_type: ProblemType
Return type:

ProblemType

class LocalILP
static from_ommx_instance(ommx_instance_buf, id_translator, *, initial_ommx_state_buf=None, num_samples=None, num_tabu_capacity=None, seed=None, zero_objective=None, sparse=None, log_cost=None, log_interval_sec=None, global_start_time=None)
Parameters:

  • ommx_instance_buf (bytes)

  • id_translator (IdTranslator)

  • initial_ommx_state_buf (Optional[bytes])

  • num_samples (Optional[int])

  • num_tabu_capacity (Optional[int])

  • seed (Optional[int])

  • zero_objective (Optional[bool])

  • sparse (Optional[bool])

  • log_cost (Optional[bool])

  • log_interval_sec (Optional[float])

  • global_start_time (Optional[float])

Return type:

LocalILP

get_operation_count()
Return type:

dict[str, int]

get_random_sampling_param()
Return type:

dict[str, int]

set_constraint_threshold(threshold)
Parameters:

threshold (float)

Return type:

None

set_random_sampling_param(param)
Parameters:

param (dict[str, int])

Return type:

None

solve(seconds, *, terminate_if_feasible=False, cancel_token=None)
Parameters:
Return type:

list[list[tuple[int, float]]]

property convergence_history: dict[str, list[float]]
Return type:

dict[str, list[float]]

class ProblemType
BINARY = Ellipsis
INTEGER = Ellipsis
MIXED_INTEGER = Ellipsis
NO_VARIABLES = Ellipsis
class State(values)
Parameters:

values (list[int])

class WeightedHillClimbing
static from_ommx_instance(ommx_instance_buf, id_translator, *, initial_ommx_state_buf=None, num_samples=None, seed=None, log_cost=None, log_interval_sec=None, global_start_time=None)
Parameters:

  • ommx_instance_buf (bytes)

  • id_translator (IdTranslator)

  • initial_ommx_state_buf (Optional[bytes])

  • num_samples (Optional[int])

  • seed (Optional[int])

  • log_cost (Optional[bool])

  • log_interval_sec (Optional[float])

  • global_start_time (Optional[float])

Return type:

WeightedHillClimbing

solve(*, num_iters, time_limit_msec_per_iter, cancel_token=None)
Parameters:
Return type:

list[list[tuple[int, float]]]

property convergence_history: dict[str, list[float]]
Return type:

dict[str, list[float]]

property weight_history: dict[str, list[list[float]]]
Return type:

dict[str, list[list[float]]]

class WeightedSimulatedAnnealing
static from_ommx_instance(ommx_instance_buf, id_translator, *, initial_ommx_state_buf=None, num_samples=None, num_iters=4, time_limit_msec_per_iter=None, count_per_iter=None, disable_annealing=False, allow_multihot_equality_move=True, allow_multihot_inequality_move=True, allow_balance_equality_move=True, allow_balance_inequality_move=True, square_linear_equality_penalty=True, square_linear_inequality_penalty=True, square_quadratic_equality_penalty=False, square_quadratic_inequality_penalty=False, normalize_coefficients=True, seed=None, log_cost=None, log_interval_sec=None, global_start_time=None)
Parameters:

  • ommx_instance_buf (bytes)

  • id_translator (IdTranslator)

  • initial_ommx_state_buf (Optional[bytes])

  • num_samples (Optional[int])

  • num_iters (Optional[int])

  • time_limit_msec_per_iter (Optional[int])

  • count_per_iter (Optional[int])

  • disable_annealing (Optional[bool])

  • allow_multihot_equality_move (Optional[bool])

  • allow_multihot_inequality_move (Optional[bool])

  • allow_balance_equality_move (Optional[bool])

  • allow_balance_inequality_move (Optional[bool])

  • square_linear_equality_penalty (Optional[bool])

  • square_linear_inequality_penalty (Optional[bool])

  • square_quadratic_equality_penalty (Optional[bool])

  • square_quadratic_inequality_penalty (Optional[bool])

  • normalize_coefficients (Optional[bool])

  • seed (Optional[int])

  • log_cost (Optional[bool])

  • log_interval_sec (Optional[float])

  • global_start_time (Optional[float])

Return type:

WeightedSimulatedAnnealing

solve(*, cancel_token=None)
Parameters:

cancel_token (Optional[jijzept_solver.solver_parameters.CancelToken])

Return type:

list[list[tuple[int, float]]]

constraint_based_move_info: dict[str, list[int]]
convergence_history: dict[str, list[float]]
weight_history: dict[str, list[list[float]]]