Solution
- class Solution
Idiomatic wrapper of
ommx.v1.Solutionprotobuf message.This also contains annotations not contained in protobuf message, and will be stored in OMMX artifact.
- __copy__() Solution
- add_user_annotation(key: str, value: str, annotation_namespace: str = 'org.ommx.user.') None
- add_user_annotations(annotations: Mapping[str, str], annotation_namespace: str = 'org.ommx.user.') None
- extract_all_decision_variables() dict
Extract all decision variables grouped by name.
Returns a mapping from variable name to a mapping from subscripts to values. This is useful for extracting all variables at once in a structured format. Variables without names are not included in the result.
Raises ValueError if a decision variable with parameters is found, or if the same name and subscript combination is found multiple times.
Examples
>>> from ommx.v1 import Instance, DecisionVariable >>> x = [DecisionVariable.binary(i, name="x", subscripts=[i]) for i in range(3)] >>> y = [DecisionVariable.binary(i+3, name="y", subscripts=[i]) for i in range(2)] >>> instance = Instance.from_components( ... decision_variables=x + y, ... objective=sum(x) + sum(y), ... constraints=[], ... sense=Instance.MAXIMIZE, ... ) >>> solution = instance.evaluate({i: 1 for i in range(5)}) >>> all_vars = solution.extract_all_decision_variables() >>> all_vars["x"] {(0,): 1.0, (1,): 1.0, (2,): 1.0} >>> all_vars["y"] {(0,): 1.0, (1,): 1.0}
- extract_all_named_functions() dict
Extract all named functions grouped by name (returns a Python dict)
- extract_constraints(name: str) dict
Extract the values of constraints based on the
namewithsubscriptskey.Raises ValueError if the constraint with parameters is found, or if the same subscript is found.
Examples
>>> from ommx.v1 import Instance, DecisionVariable >>> x = [DecisionVariable.binary(i) for i in range(3)] >>> c0 = (x[0] + x[1] == 1).add_name("c").add_subscripts([0]) >>> c1 = (x[1] + x[2] == 1).add_name("c").add_subscripts([1]) >>> instance = Instance.from_components( ... decision_variables=x, ... objective=sum(x), ... constraints=[c0, c1], ... sense=Instance.MAXIMIZE, ... ) >>> solution = instance.evaluate({0: 1, 1: 0, 2: 1}) >>> solution.extract_constraints("c") {(0,): 0.0, (1,): 0.0}
- extract_decision_variables(name: str) dict
Extract the values of decision variables based on the
namewithsubscriptskey.Raises ValueError if a decision variable with parameters is found, or if the same subscript is found.
Examples
>>> from ommx.v1 import Instance, DecisionVariable >>> x = [DecisionVariable.binary(i, name="x", subscripts=[i]) for i in range(3)] >>> instance = Instance.from_components( ... decision_variables=x, ... objective=sum(x), ... constraints=[sum(x) == 1], ... sense=Instance.MAXIMIZE, ... ) >>> solution = instance.evaluate({i: 1 for i in range(3)}) >>> solution.extract_decision_variables("x") {(0,): 1.0, (1,): 1.0, (2,): 1.0}
- extract_named_functions(name: str) dict
Extract named functions by name with subscripts as key (returns a Python dict)
- from_bytes(bytes: bytes) Solution
- get_constraint_by_id(constraint_id: int) EvaluatedConstraint
Get a specific evaluated constraint by ID
- get_constraint_value(constraint_id: int) float
Get the evaluated value of a specific constraint by ID
- get_decision_variable_by_id(variable_id: int) EvaluatedDecisionVariable
Get a specific evaluated decision variable by ID
- get_dual_variable(constraint_id: int) Optional[float]
Get the dual variable value for a specific constraint by ID
- get_named_function_by_id(named_function_id: int) EvaluatedNamedFunction
Get a specific evaluated named function by ID
- get_user_annotation(key: str, annotation_namespace: str = 'org.ommx.user.') str
- get_user_annotations(annotation_namespace: str = 'org.ommx.user.') dict[str, str]
- set_dual_variable(constraint_id: int, value: Optional[float]) None
Set the dual variable value for a specific constraint by ID
- to_bytes() bytes
- total_violation_l1() float
Calculate total constraint violation using L1 norm (sum of absolute violations)
Returns the sum of violations across all constraints (including removed constraints):
For equality constraints: \(\sum |f(x)|\)
For inequality constraints: \(\sum \max(0, f(x))\)
- total_violation_l2() float
Calculate total constraint violation using L2 norm squared (sum of squared violations)
Returns the sum of squared violations across all constraints (including removed constraints):
For equality constraints: \(\sum (f(x))^2\)
For inequality constraints: \(\sum (\max(0, f(x)))^2\)
- LP_RELAXED: Relaxation
Class constant for LP-relaxed solutions
- NOT_OPTIMAL: Optimality
Class constant for non-optimal solutions
- OPTIMAL: Optimality
Class constant for optimal solutions
- property annotations: dict[str, str]
Returns a copy of the annotations dictionary.
Mutating the returned dict will not update the object. Use
add_user_annotation()or assign toannotationsto modify annotations.
- property constraint_ids: set[int]
Read-only property.
- property constraints: list[EvaluatedConstraint]
Read-only property.
Get evaluated constraints as a list sorted by ID
- property constraints_df: DataFrame
Read-only property.
DataFrame of evaluated constraints
Columns: id (index), equality, value, used_ids, name, subscripts, description, dual_variable, removed_reason
- property decision_variable_ids: set[int]
Read-only property.
- property decision_variable_names: set[str]
Read-only property.
Get all unique decision variable names in this solution.
Returns a set of all unique variable names. Variables without names are not included.
Examples
>>> from ommx.v1 import Instance, DecisionVariable >>> x = [DecisionVariable.binary(i, name="x", subscripts=[i]) for i in range(3)] >>> y = [DecisionVariable.binary(i+3, name="y", subscripts=[i]) for i in range(2)] >>> instance = Instance.from_components( ... decision_variables=x + y, ... objective=sum(x) + sum(y), ... constraints=[], ... sense=Instance.MAXIMIZE, ... ) >>> solution = instance.evaluate({i: 1 for i in range(5)}) >>> sorted(solution.decision_variable_names) ['x', 'y']
- property decision_variables: list[EvaluatedDecisionVariable]
Read-only property.
Get evaluated decision variables as a list sorted by ID
- property decision_variables_df: DataFrame
Read-only property.
DataFrame of evaluated decision variables
Columns: id (index), kind, lower, upper, name, subscripts, description, substituted_value, value
- property feasible: bool
Read-only property.
Feasibility of the solution in terms of all constraints, including removed constraints.
This is an alias for
feasible_unrelaxed.Compatibility: The meaning of this property has changed from Python SDK 1.7.0. Previously, this property represents the feasibility of the remaining constraints only, i.e. excluding relaxed constraints. From Python SDK 1.7.0, this property represents the feasibility of all constraints, including relaxed constraints.
- property feasible_relaxed: bool
Read-only property.
Feasibility of the solution in terms of remaining constraints, not including relaxed (removed) constraints.
- property feasible_unrelaxed: bool
Read-only property.
Feasibility of the solution in terms of all constraints, including relaxed (removed) constraints.
- property named_function_ids: set[int]
Read-only property.
- property named_function_names: set[str]
Read-only property.
Get all unique named function names in this solution
- property named_functions: list[EvaluatedNamedFunction]
Read-only property.
Get evaluated named functions as a list sorted by ID
- property named_functions_df: DataFrame
Read-only property.
DataFrame of evaluated named functions
Columns: id (index), value, used_ids, name, subscripts, description, parameters.{key}
- property objective: float
Read-only property.
Get the objective function value
- property optimality: Optimality
Get the optimality status
- property relaxation: Relaxation
Get the relaxation status
- property sense: Sense
Read-only property.
Get the optimization sense (minimize or maximize)