ommx_highs_adapter.adapter#

Classes#

OMMXHighsAdapter

OMMX Adapter for HiGHS solver.

Module Contents#

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

OMMX Adapter for HiGHS solver.

This adapter translates OMMX optimization problems (ommx.v1.Instance) into HiGHS-compatible formats and converts HiGHS solutions back to OMMX format (ommx.v1.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
`DecisionVariable.BINARY`	`HighsVarType.kInteger`	`[0, 1]`
`DecisionVariable.INTEGER`	`HighsVarType.kInteger`	`[var.bound.lower, var.bound.upper]`
`DecisionVariable.CONTINUOUS`	`HighsVarType.kContinuous`	`[var.bound.lower, var.bound.upper]`
`DecisionVariable.SEMI_INTEGER`	Not supported (support planned)	-
`DecisionVariable.SEMI_CONTINUOUS`	Not supported (support planned)	-

Note: Semi-integer and semi-continuous variables are planned for future support but are currently unsupported. Using these variable types will raise an OMMXHighsAdapterError.

Constraints#

Supported Function Types:

Constant functions (ommx.v1.Function.constant)
Linear functions (ommx.v1.Function.linear)

Constraint Types:

OMMX Constraint	Mathematical Form	HiGHS Constraint
`Constraint.EQUAL_TO_ZERO`	f(x) = 0	`const_expr == 0`
`Constraint.LESS_THAN_OR_EQUAL_TO_ZERO`	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
`Instance.MINIMIZE`	`model.minimize(...)`
`Instance.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: Set to OPTIMALITY_OPTIMAL when HiGHS returns kOptimal

Dual Variables: Extracted from solution.row_dual for constraints

Error Handling#

Unsupported Features:

Quadratic functions (HiGHS supports linear problems only)
Semi-integer variables (DecisionVariable.SEMI_INTEGER, kind=4) - support planned
Semi-continuous variables (DecisionVariable.SEMI_CONTINUOUS, kind=5) - support planned
Constraint types other than EQUAL_TO_ZERO/LESS_THAN_OR_EQUAL_TO_ZERO

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
- Semi-integer (SEMI_INTEGER) support is planned but not yet implemented
- Semi-continuous (SEMI_CONTINUOUS) support is planned but not yet implemented

Examples#

>>> from ommx_highs_adapter import OMMXHighsAdapter
>>> from ommx.v1 import Instance, DecisionVariable
>>>
>>> # 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=Instance.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}

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 dual variable information.

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 - Dual variables for linear constraints

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.

The dual variables are extracted from HiGHS’s row_dual and mapped to OMMX constraints based on their order. Only constraints with valid dual information will have the dual_variable field set.

Examples#

>>> from ommx_highs_adapter import OMMXHighsAdapter
>>> from ommx.v1 import Instance, DecisionVariable
>>>
>>> x = DecisionVariable.binary(0)
>>> instance = Instance.from_components(
...     decision_variables=[x],
...     objective=x,
...     constraints={},
...     sense=Instance.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.v1 import Instance, DecisionVariable
>>>
>>> x1 = DecisionVariable.integer(1, lower=0, upper=5)
>>> instance = Instance.from_components(
...     decision_variables=[x1],
...     objective=x1,
...     constraints={},
...     sense=Instance.MINIMIZE,
... )
>>> adapter = OMMXHighsAdapter(instance)
>>> model = adapter.solver_input
>>> model.run()  
<...>
>>> state = adapter.decode_to_state(model)
>>> state.entries
{1: 0.0}

classmethod solve(ommx_instance: Instance, *, verbose: bool = False) → 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. Must satisfy HiGHS adapter requirements: linear objective function (constant or linear terms only), linear constraints (constant or linear terms only), variables of type Binary, Integer, or Continuous only (Semi-integer and Semi-continuous support is planned but not yet implemented), and constraints of type EQUAL_TO_ZERO or LESS_THAN_OR_EQUAL_TO_ZERO only.
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 (if available) in constraint.dual_variable

Raises#

InfeasibleDetected: When the optimization problem has no feasible solution
UnboundedDetected: When the optimization problem is unbounded
OMMXHighsAdapterError: When the problem contains unsupported features or HiGHS encounters an error

Examples#

Knapsack Problem

>>> from ommx.v1 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=Instance.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=Instance.MAXIMIZE,
... )
>>> OMMXHighsAdapter.solve(instance)  
Traceback (most recent call last):
    ...
ommx.adapter.InfeasibleDetected: Model was infeasible

ADDITIONAL_CAPABILITIES: frozenset[AdditionalCapability]#

highs_vars = []#

instance#

model#

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

var_ids#