ommx_openjij_adapter.adapter ============================ .. py:module:: ommx_openjij_adapter.adapter .. autoapi-nested-parse:: Direct OpenJij Adapter implementation. Classes ------- .. autoapisummary:: ommx_openjij_adapter.adapter.OMMXOpenJijSAAdapter Module Contents --------------- .. py:class:: OMMXOpenJijSAAdapter(ommx_instance: ommx.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 :attr:`OpenJijPreparation.input` back to this Adapter as a separate :class:`ommx.Instance` value. .. py:method:: check_applicability(ommx_instance: ommx.Instance) -> AdapterApplicabilityReport :classmethod: 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. .. py:method:: check_preparation(ommx_instance: ommx.Instance, *, config: ommx_openjij_adapter._preparation.OpenJijPreparationConfig | None = None) -> ommx_openjij_adapter._preparation.OpenJijPreparationReport :classmethod: Dry-run the complete explicit preparation without mutating the input. This is intentionally separate from :meth:`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 :class:`~ommx.InfeasibleDetected`. Approximate integer slack is disabled unless the supplied :class:`OpenJijPreparationConfig` enables it. .. py:method:: decode(data: openjij.Response) -> ommx.Solution .. py:method:: decode_to_samples(data: openjij.Response) -> ommx.Samples Convert `openjij.Response `_ to :class:`Samples` There is a static method :meth:`decode_to_samples` that does the same thing. .. py:method:: decode_to_sampleset(data: openjij.Response) -> ommx.SampleSet .. py:method:: prepare(ommx_instance: ommx.Instance, *, config: ommx_openjij_adapter._preparation.OpenJijPreparationConfig | None = None) -> ommx_openjij_adapter._preparation.OpenJijPreparation :classmethod: Produce a separate Adapter input and an auditable preparation report. Raises :class:`~ommx.InfeasibleDetected` when variable bounds prove an inequality infeasible. Other preparation failures raise :class:`OpenJijPreparationError`. Approximate integer slack is used only when the supplied :class:`OpenJijPreparationConfig` enables it. .. py:method:: require_applicable(ommx_instance: ommx.Instance) -> AdapterApplicabilityReport :classmethod: Return the report or raise :class:`AdapterNotApplicableError`. .. py:method:: sample(ommx_instance: ommx.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: ommx.adapter.DiagnosticsSink | None = None) -> ommx.SampleSet :classmethod: Sample the exact applicable ``ommx_instance`` passed to the Adapter. .. py:method:: solve(ommx_instance: ommx.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: ommx.adapter.DiagnosticsSink | None = None) -> ommx.Solution :classmethod: Return the best feasible sample from :meth:`sample`. .. py:attribute:: INPUT_CLASS :type: ClassVar[ommx.InstanceClass | None] .. py:attribute:: MAX_OPENJIJ_VARIABLE_ID :type: ClassVar[int] :value: 9223372036854775807 .. py:attribute:: beta_max :type: float | None :value: None maximum value of inverse temperature .. py:attribute:: beta_min :type: float | None :value: None minimal value of inverse temperature .. py:attribute:: initial_state :type: list | dict | None :value: None initial state (parameter only used if problem is QUBO) .. py:attribute:: num_reads :type: int | None :value: None number of reads .. py:attribute:: num_sweeps :type: int | None :value: None number of sweeps .. py:attribute:: ommx_instance :type: ommx.Instance Isolated copy of the exact Adapter input used to evaluate returned samples. .. py:attribute:: reinitialize_state :type: bool | None :value: None if true reinitialize state for each run (parameter only used if problem is QUBO) .. py:property:: sampler_input :type: dict[tuple[int, Ellipsis], float] .. py:attribute:: schedule :type: list | None :value: None list of inverse temperature (parameter only used if problem is QUBO) .. py:attribute:: seed :type: int | None :value: None seed for Monte Carlo algorithm .. py:property:: solver_input :type: dict[tuple[int, Ellipsis], float] .. py:attribute:: sparse :type: bool | None :value: None use sparse matrix or not (parameter only used if problem is QUBO) .. py:attribute:: updater :type: str | None :value: None updater algorithm