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. :meth:`sample` and :meth:`solve` prepare an isolated copy with :meth:`recommended_preparation_policy`. Use :meth:`sample_without_preparation` or :meth:`solve_without_preparation` after explicitly preparing an instance when caller-owned choices such as fixed penalty magnitudes are required. .. py:method:: check_applicability(ommx_instance: ommx.Instance) -> ommx.InstanceClassMembershipReport :classmethod: Check ``INPUT_CLASS`` membership without mutation. .. py:method:: decode(data: openjij.Response) -> ommx.Solution Decode sampler output and return its best feasible 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:: recommended_preparation_policy() -> ommx.PreparationPolicy :classmethod: Recommend the model changes commonly needed by OpenJij. The recommendation lowers every special-constraint family, converts the active objective to minimization, adds Integer slack while permitting an inequality to remain when exact equality conversion is unavailable, and log-encodes every used Integer variable. Both Integer slack ranges use 32. Fixed penalty magnitudes remain explicit caller parameters because sufficient values depend on the application. The shared ``Instance`` owner operation validates their nonnegative-with-tolerance domain. Set ``fixed_penalty`` on the fresh returned policy when active constraints must be removed. .. py:method:: require_applicable(ommx_instance: ommx.Instance) -> ommx.InstanceClassMembershipReport :classmethod: Return the membership report or raise ``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, 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: Prepare and sample an isolated copy of ``ommx_instance``. OpenJij ``initial_state`` values are defined against the solver-variable representation of an exact Adapter input, so they are accepted only by :meth:`sample_without_preparation`. .. py:method:: sample_without_preparation(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 an exact OpenJij Adapter input without preparing it. ``initial_state`` is defined against this exact input's solver-variable representation. .. 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, 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: Prepare, sample, and return the best feasible result. OpenJij ``initial_state`` values are defined against the solver-variable representation of an exact Adapter input, so they are accepted only by :meth:`solve_without_preparation`. .. py:method:: solve_without_preparation(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 result from :meth:`sample_without_preparation`. ``initial_state`` is defined against the exact Adapter input's solver-variable representation. .. py:attribute:: INPUT_CLASS :type: ClassVar[ommx.InstanceClass] Required condition for an exact Adapter input. .. 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, ...], 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, ...], float] Expose :attr:`sampler_input` through the SolverAdapter interface. .. 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