ommx_openjij_adapter.adapter

Direct OpenJij Adapter implementation.

Classes

OMMXOpenJijSAAdapter

Sample an applicable Binary polynomial input with OpenJij simulated annealing.

Module Contents

class OMMXOpenJijSAAdapter(ommx_instance: Instance, *, beta_min: float | None = None, beta_max: float | None = None, num_sweeps: int | None = None, num_reads: int | None = None, schedule: list | None = None, initial_state: list | dict | None = None, updater: str | None = None, sparse: bool | None = None, reinitialize_state: bool | None = None, seed: int | None = None)

Sample an applicable Binary polynomial input with OpenJij simulated annealing.

The direct Adapter input must use only Binary decision variables, have no active regular or special constraints, and be a minimization problem. Arbitrary polynomial objective degree is supported through OpenJij’s QUBO and Binary-HUBO paths.

Integer encoding, sense reversal, slack introduction, and finite constraint penalties are explicit preparation operations, not part of the declared input class. Pass OpenJijPreparation.input back to this Adapter as a separate ommx.Instance value.

classmethod check_applicability(ommx_instance: Instance) → AdapterApplicabilityReport

Inspect applicability without mutating or preparing ommx_instance.

Adapter-specific preconditions run only after at least one complete input-class clause contains the instance. The hook receives an isolated copy so it cannot mutate the caller’s instance. Any explicitly transformed value is a different input and must be checked separately.

classmethod check_preparation(ommx_instance: Instance, *, config: ommx_openjij_adapter._preparation.OpenJijPreparationConfig | None = None) → ommx_openjij_adapter._preparation.OpenJijPreparationReport

Dry-run the complete explicit preparation without mutating the input.

This is intentionally separate from check_applicability(), which checks only the Binary, unconstrained minimization Adapter input. The 53-bit log-encoding limit describes availability of that preparation operation, not an OpenJij input-class condition and not an ommx.v2.Feature. A model proven infeasible while preparing integer slack raises InfeasibleDetected. Approximate integer slack is disabled unless the supplied OpenJijPreparationConfig enables it.

decode(data: openjij.Response) → Solution

decode_to_samples(data: openjij.Response) → Samples

Convert openjij.Response to Samples

There is a static method decode_to_samples() that does the same thing.

decode_to_sampleset(data: openjij.Response) → SampleSet

classmethod prepare(ommx_instance: Instance, *, config: ommx_openjij_adapter._preparation.OpenJijPreparationConfig | None = None) → ommx_openjij_adapter._preparation.OpenJijPreparation

Produce a separate Adapter input and an auditable preparation report.

Raises InfeasibleDetected when variable bounds prove an inequality infeasible. Other preparation failures raise OpenJijPreparationError. Approximate integer slack is used only when the supplied OpenJijPreparationConfig enables it.

classmethod require_applicable(ommx_instance: Instance) → AdapterApplicabilityReport

Return the report or raise AdapterNotApplicableError.

classmethod sample(ommx_instance: Instance, *, beta_min: float | None = None, beta_max: float | None = None, num_sweeps: int | None = None, num_reads: int | None = None, schedule: list | None = None, initial_state: list | dict | None = None, updater: str | None = None, sparse: bool | None = None, reinitialize_state: bool | None = None, seed: int | None = None, diagnostics: DiagnosticsSink | None = None) → SampleSet

Sample the exact applicable ommx_instance passed to the Adapter.

classmethod solve(ommx_instance: Instance, *, beta_min: float | None = None, beta_max: float | None = None, num_sweeps: int | None = None, num_reads: int | None = None, schedule: list | None = None, initial_state: list | dict | None = None, updater: str | None = None, sparse: bool | None = None, reinitialize_state: bool | None = None, seed: int | None = None, diagnostics: DiagnosticsSink | None = None) → Solution

Return the best feasible sample from sample().

INPUT_CLASS: ClassVar[InstanceClass | None]

MAX_OPENJIJ_VARIABLE_ID: ClassVar[int] = 9223372036854775807

beta_max: float | None = None

maximum value of inverse temperature

beta_min: float | None = None

minimal value of inverse temperature

initial_state: list | dict | None = None

initial state (parameter only used if problem is QUBO)

num_reads: int | None = None

number of reads

num_sweeps: int | None = None

number of sweeps

ommx_instance: Instance

Isolated copy of the exact Adapter input used to evaluate returned samples.

reinitialize_state: bool | None = None

if true reinitialize state for each run (parameter only used if problem is QUBO)

property sampler_input: dict[tuple[int, Ellipsis], float]

schedule: list | None = None

list of inverse temperature (parameter only used if problem is QUBO)

seed: int | None = None

seed for Monte Carlo algorithm

property solver_input: dict[tuple[int, Ellipsis], float]

sparse: bool | None = None

use sparse matrix or not (parameter only used if problem is QUBO)

updater: str | None = None

updater algorithm