ommx_highs_adapter.adapter

ommx_highs_adapter.adapter#

Classes#

`HighsDiagnosticsAnalyzer`	Post-processor for HiGHS diagnostics.
`HighsProgressSnapshot`	HiGHS MIP solve progress observed from one logging callback.
`HighsTerminationReport`	HiGHS-side termination summary recorded after `model.run()`.
`OMMXHighsAdapter`	OMMX Adapter for HiGHS solver.

Module Contents#

class HighsDiagnosticsAnalyzer(diagnostics: Iterable[Any])#

Post-processor for HiGHS diagnostics.

The analyzer accepts either typed diagnostics collected by ommx.adapter.DiagnosticCollector or dictionaries loaded from ommx.experiment.Solve.diagnostics.

property dual_bound: Any#: Return MIP dual bounds indexed by solving time.

property event: Any#: Return progress event names indexed by solving time.

property gap: Any#: Return MIP gaps indexed by solving time.

property ipm_iteration_count: Any#: Return interior-point iteration counts indexed by solving time.

property mip_dual_bound: Any#: Alias for dual_bound.

property mip_gap: Any#: Alias for gap.

property mip_node_count: Any#: Return MIP node counts indexed by solving time.

property mip_primal_bound: Any#: Alias for primal_bound.

property node_count: Any#: Alias for mip_node_count.

property objective_value: Any#: Return objective values indexed by solving time.

property pdlp_iteration_count: Any#: Return PDLP iteration counts indexed by solving time.

property primal_bound: Any#: Return MIP primal bounds indexed by solving time.

property progress_history_df: Any#: Return progress snapshots as a pandas DataFrame indexed by time.

property progress_history_records: list[dict[str, object]]#: Return one dictionary per HiGHS MIP logging callback.

property progress_snapshots: tuple[HighsProgressSnapshot, ...]#: Typed progress snapshots in the order they were recorded.

property simplex_iteration_count: Any#: Return simplex iteration counts indexed by solving time.

property termination_mip_dual_bound: float | None#: Return the terminal HiGHS MIP dual bound, if present.

property termination_mip_gap: float | None#: Return the terminal HiGHS MIP gap, if present.

property termination_mip_node_count: int | None#: Return the terminal HiGHS MIP node count, if present.

property termination_objective_value: float | None#: Return the terminal objective value, if present.

property termination_result: dict[str, object] | None#: Return the terminal HiGHS report as one dictionary, if present.

property termination_status: str | None#: Return the terminal HiGHS model status, if present.

class HighsProgressSnapshot#

HiGHS MIP solve progress observed from one logging callback.

classmethod from_callback_event(event: Any) → HighsProgressSnapshot#

dual_bound: float#: MIP dual bound reported at the callback.

event: str#: HiGHS callback message, currently "MIP logging".

gap: float#: MIP relative gap reported at the callback.

ipm_iteration_count: int#: Interior-point iterations at the callback.

mip_node_count: int#: MIP branch-and-bound nodes processed at the callback.

objective_value: float#: Objective value reported at the callback.

pdlp_iteration_count: int#: PDLP iterations at the callback.

primal_bound: float#: MIP primal bound reported at the callback.

simplex_iteration_count: int#: Simplex iterations at the callback.

solving_time_sec: float#: HiGHS runtime when the callback ran.

class HighsTerminationReport#

HiGHS-side termination summary recorded after model.run().

The HiGHS adapter records this report before decoding the optimized HiGHS model back into an OMMX solution. It is therefore available even when decoding raises an adapter exception such as infeasible or unbounded detection.

classmethod from_model(model: highspy.Highs) → HighsTerminationReport#

crossover_iteration_count: int#: Number of crossover iterations after interior-point optimization.

dual_solution_status: int#: HiGHS dual solution status code.

highs_githash: str#: HiGHS git hash reported by highspy.

highs_version: str#: HiGHS version used through highspy.

ipm_iteration_count: int#: Number of interior-point iterations.

max_dual_infeasibility: float#: Maximum dual infeasibility in the final solution.

max_integrality_violation: float#: Maximum integrality violation in the final solution.

max_primal_infeasibility: float#: Maximum primal infeasibility in the final solution.

mip_dual_bound: float#: MIP dual bound reported by HighsInfo.mip_dual_bound.

mip_gap: float#: MIP relative gap reported by HighsInfo.mip_gap.

mip_node_count: int#: Number of MIP branch-and-bound nodes processed by HiGHS.

objective_value: float | None#: Objective value reported by HiGHS, or None when no objective is valid.

pdlp_iteration_count: int#: Number of PDLP iterations.

primal_dual_integral: float#: HiGHS primal-dual integral.

primal_solution_status: int#: HiGHS primal solution status code.

run_time_sec: float#: HiGHS runtime in seconds.

simplex_iteration_count: int#: Number of simplex iterations.

status: str#: HiGHS model status, such as "Optimal" or "Infeasible".

class OMMXHighsAdapter(ommx_instance: Instance, *, verbose: bool = False)#

OMMX Adapter for HiGHS solver.

This adapter translates OMMX optimization problems (ommx.Instance) into HiGHS-compatible formats and converts HiGHS solutions back to OMMX format (ommx.Solution).

Translation Specifications#

Decision Variables#

The adapter handles the following translations for decision variables:

ID Management:

OMMX: Variables managed by IDs (non-sequential integers)
HiGHS: Variables managed by array indices (0-based sequential)
Mapping maintained internally for bidirectional conversion

Variable Types:

OMMX Type	HiGHS Type	Bounds
`Kind.Binary`	`HighsVarType.kInteger`	`[0, 1]`
`Kind.Integer`	`HighsVarType.kInteger`	`[var.bound.lower, var.bound.upper]`
`Kind.Continuous`	`HighsVarType.kContinuous`	`[var.bound.lower, var.bound.upper]`
`Kind.SemiInteger`	Not supported (support planned)	-
`Kind.SemiContinuous`	Not supported (support planned)	-

Note: Semi-integer and semi-continuous variables are planned for future support but are currently unsupported. Exact-input APIs reject these kinds with AdapterNotApplicableError; the easy API reports that recommended Preparation could not reach INPUT_CLASS.

Constraints#

Supported Function Types:

Constant functions (ommx.Function.constant)
Linear functions (ommx.Function.linear)

Constraint Types:

OMMX Constraint	Mathematical Form	HiGHS Constraint
`Equality.EqualToZero`	f(x) = 0	`const_expr == 0`
`Equality.LessThanOrEqualToZero`	f(x) ≤ 0	`const_expr <= 0`

Constant Constraint Handling:

Equality: Skip if |constant| ≤ 1e-10, error if |constant| > 1e-10
Inequality: Skip if constant ≤ 1e-10, error if constant > 1e-10

Constraint ID Management:

OMMX constraint IDs converted to HiGHS constraint names via str(constraint.id)

Objective Function#

Optimization Direction:

OMMX Direction	HiGHS Method
`Sense.Minimize`	`model.minimize(...)`
`Sense.Maximize`	`model.maximize(...)`

Function Types:

Constant objectives: Processing skipped
Linear objectives: Converted to HiGHS linear expressions

Solution Decoding#

Variable Values: Extracted from HiGHS solution.col_value using maintained ID mapping

Optimality Status:

A HiGHS kOptimal status becomes OPTIMALITY_UNSPECIFIED when it does not transport to the output objective

Dual Variables: Extracted from solution.row_dual when the active objective is also the output objective. They are omitted when output-objective projection would require a dual mapping that OMMX does not define.

Error Handling#

Unsupported Features:

Quadratic functions (HiGHS supports linear problems only)
Semi-integer variables (Kind.SemiInteger) - support planned
Semi-continuous variables (Kind.SemiContinuous) - support planned
Constraint types other than Equality.EqualToZero/ Equality.LessThanOrEqualToZero

Solver Status Mapping:

HiGHS Status	Exception
`kInfeasible`	`InfeasibleDetected`
`kUnbounded`	`UnboundedDetected`
`kNotset`	`OMMXHighsAdapterError`

Limitations#

Linear problems only (no quadratic constraints or objectives)
Constraint forms limited to equality (= 0) and inequality (≤ 0)
Variable types limited to Binary, Integer, and Continuous
- Kind.SemiInteger support is planned but not yet implemented
- Kind.SemiContinuous support is planned but not yet implemented

Examples#

>>> from ommx_highs_adapter import OMMXHighsAdapter
>>> from ommx import DecisionVariable, Instance, Sense
>>>
>>> # Define problem
>>> x = DecisionVariable.binary(0)
>>> y = DecisionVariable.integer(1, lower=0, upper=10)
>>> instance = Instance.from_components(
...     decision_variables=[x, y],
...     objective=2*x + 3*y,
...     constraints={0: x + y <= 5},
...     sense=Sense.Maximize,
... )
>>>
>>> # Solve
>>> solution = OMMXHighsAdapter.solve(instance)
>>> print(f"Optimal value: {solution.objective}")
Optimal value: 15.0
>>> print(f"Variables: {solution.state.entries}")
Variables: {0: 0.0, 1: 5.0}

classmethod check_applicability(ommx_instance: Instance) → InstanceClassMembershipReport#: Check INPUT_CLASS membership without mutation.

decode(data: highspy.Highs) → Solution#

Convert an optimized HiGHS model back to an OMMX Solution.

This method translates HiGHS solver results into OMMX format, including variable values, optimality status, and transportable dual variable information. Backend optimality is mapped through the instance’s output-objective semantics and remains unspecified when it does not transport.

Parameters#

datahighspy.Highs: The HiGHS model that has been optimized. Must be the same model returned by solver_input property.

Returns#

Solution: Complete OMMX solution containing: - Variable values mapped back to original OMMX IDs - Constraint evaluations and feasibility status - Optimality information from HiGHS when transportable to the output objective - Dual variables for linear constraints without output-objective projection

Raises#

OMMXHighsAdapterError: If the model has not been optimized yet
InfeasibleDetected: If HiGHS determined the problem is infeasible
UnboundedDetected: If HiGHS determined the problem is unbounded

Notes#

This method should only be used after solving the model with HiGHS. Any modifications to the HiGHS model structure after creation may make the decoding process incompatible.

When the active objective is also the output objective, dual variables are extracted from HiGHS’s row_dual and mapped to OMMX constraints based on their order. If output_objective is present, every dual is omitted because OMMX does not define how the active formulation’s dual certificate maps to the projected output semantics.

Examples#

>>> from ommx_highs_adapter import OMMXHighsAdapter
>>> from ommx import Instance, DecisionVariable
>>>
>>> x = DecisionVariable.binary(0)
>>> instance = Instance.from_components(
...     decision_variables=[x],
...     objective=x,
...     constraints={},
...     sense=Sense.Maximize,
... )
>>>
>>> adapter = OMMXHighsAdapter(instance)
>>> model = adapter.solver_input
>>> model.run()  
<...>
>>> solution = adapter.decode(model)
>>> solution.objective
1.0

decode_to_state(data: highspy.Highs) → State#

Extract variable values from an optimized HiGHS model as an OMMX State.

Parameters#

datahighspy.Highs: The optimized HiGHS model

Returns#

State: OMMX state containing variable values mapped to original OMMX IDs

Raises#

OMMXHighsAdapterError: If the model has not been optimized
InfeasibleDetected: If the model is infeasible
UnboundedDetected: If the model is unbounded

Examples#

>>> from ommx_highs_adapter import OMMXHighsAdapter
>>> from ommx import Instance, DecisionVariable
>>>
>>> x1 = DecisionVariable.integer(1, lower=0, upper=5)
>>> instance = Instance.from_components(
...     decision_variables=[x1],
...     objective=x1,
...     constraints={},
...     sense=Sense.Minimize,
... )
>>> adapter = OMMXHighsAdapter(instance)
>>> model = adapter.solver_input
>>> model.run()  
<...>
>>> state = adapter.decode_to_state(model)
>>> state.entries
{1: 0.0}

classmethod recommended_preparation_policy() → PreparationPolicy#

Recommend lowering special constraints before using HiGHS.

HiGHS accepts both optimization senses, Binary, Integer, and Continuous variables, and linear regular constraints directly. Its recommendation therefore enables only lowering for the special-constraint families currently understood by OMMX. The easy API applies a fresh policy to an isolated copy; callers may instead edit and apply one before invoking solve_without_preparation().

classmethod require_applicable(ommx_instance: Instance) → InstanceClassMembershipReport#: Return the membership report or raise AdapterNotApplicableError.

classmethod solve(ommx_instance: Instance, *, verbose: bool = False, diagnostics: DiagnosticsSink | None = None) → Solution#

Solve an OMMX optimization problem using HiGHS solver.

This method provides a convenient interface for solving optimization problems without needing to manually instantiate the adapter. It handles the complete workflow: translation to HiGHS format, solving, and result conversion.

Parameters#

ommx_instanceInstance: The OMMX optimization problem to solve. The input is not modified; an isolated copy is prepared with the recommended HiGHS policy.
verbosebool, default=False: If True, enable HiGHS’s console logging for debugging

Returns#

Solution: The solution containing: - Variable values in solution.state.entries - Objective value in solution.objective - Constraint evaluations in solution.constraints - Optimality status in solution.optimality - Dual variables when the active and output objectives coincide

Raises#

InfeasibleDetected: When the optimization problem has no feasible solution
UnboundedDetected: When the optimization problem is unbounded
PreparationTargetNotReachedError: When the recommended preparation cannot reach the Adapter input class
OMMXHighsAdapterError: When conversion or HiGHS encounters an adapter-specific error

Examples#

Knapsack Problem

>>> from ommx import Instance, DecisionVariable, Solution
>>> from ommx_highs_adapter import OMMXHighsAdapter
>>>
>>> p = [10, 13, 18, 32, 7, 15]  # profits
>>> w = [11, 15, 20, 35, 10, 33]  # weights
>>> x = [DecisionVariable.binary(i) for i in range(6)]
>>> instance = Instance.from_components(
...     decision_variables=x,
...     objective=sum(p[i] * x[i] for i in range(6)),
...     constraints={0: sum(w[i] * x[i] for i in range(6)) <= 47},
...     sense=Sense.Maximize,
... )
>>>
>>> solution = OMMXHighsAdapter.solve(instance)
>>> sorted([(id, value) for id, value in solution.state.entries.items()])
[(0, 1.0), (1, 0.0), (2, 0.0), (3, 1.0), (4, 0.0), (5, 0.0)]
>>> solution.feasible
True
>>> assert solution.optimality == Solution.OPTIMAL
>>> solution.objective
42.0

Infeasible Problem

>>> x = DecisionVariable.integer(0, upper=3, lower=0)
>>> instance = Instance.from_components(
...     decision_variables=[x],
...     objective=x,
...     constraints={0: x >= 4},  # Impossible: x ≤ 3 and x ≥ 4
...     sense=Sense.Maximize,
... )
>>> OMMXHighsAdapter.solve(instance)  
Traceback (most recent call last):
    ...
ommx.InfeasibleDetected: Model was infeasible

classmethod solve_without_preparation(ommx_instance: Instance, *, verbose: bool = False, diagnostics: DiagnosticsSink | None = None) → Solution#: Solve an exact HiGHS Adapter input without preparing it.

INPUT_CLASS: ClassVar[InstanceClass]#: Required condition for an exact Adapter input.

property solver_input: highspy.Highs#

The HiGHS model generated from the OMMX instance.

Returns#

highspy.Highs: The HiGHS model ready for optimization. This model contains: - Decision variables translated from OMMX IDs to HiGHS indices - Constraints converted to HiGHS linear expressions - Objective function set according to optimization direction