83 lines
2.6 KiB
Plaintext
83 lines
2.6 KiB
Plaintext
---
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title: LinearEqualityToPenalty
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description: API reference for qiskit.optimization.converters.LinearEqualityToPenalty
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in_page_toc_min_heading_level: 1
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python_api_type: class
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python_api_name: qiskit.optimization.converters.LinearEqualityToPenalty
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---
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# LinearEqualityToPenalty
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<Class id="qiskit.optimization.converters.LinearEqualityToPenalty" isDedicatedPage={true} github="https://github.com/qiskit-community/qiskit-aqua/tree/stable/0.9/qiskit/optimization/converters/linear_equality_to_penalty.py" signature="LinearEqualityToPenalty(penalty=None)" modifiers="class">
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Bases: `qiskit.optimization.converters.quadratic_program_converter.QuadraticProgramConverter`
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Convert a problem with only equality constraints to unconstrained with penalty terms.
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**Parameters**
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**penalty** (`Optional`\[`float`]) – Penalty factor to scale equality constraints that are added to objective. If None is passed, penalty factor will be automatically calculated.
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## Methods
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### convert
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<Function id="qiskit.optimization.converters.LinearEqualityToPenalty.convert" signature="LinearEqualityToPenalty.convert(problem)">
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Convert a problem with equality constraints into an unconstrained problem.
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**Parameters**
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**problem** (`QuadraticProgram`) – The problem to be solved, that does not contain inequality constraints.
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**Return type**
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`QuadraticProgram`
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**Returns**
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The converted problem, that is an unconstrained problem.
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**Raises**
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[**QiskitOptimizationError**](qiskit.optimization.QiskitOptimizationError "qiskit.optimization.QiskitOptimizationError") – If an inequality constraint exists.
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</Function>
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### interpret
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<Function id="qiskit.optimization.converters.LinearEqualityToPenalty.interpret" signature="LinearEqualityToPenalty.interpret(x)">
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Convert the result of the converted problem back to that of the original problem
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**Parameters**
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**x** (`Union`\[`ndarray`, `List`\[`float`]]) – The result of the converted problem or the given result in case of FAILURE.
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**Return type**
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`ndarray`
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**Returns**
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The result of the original problem.
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**Raises**
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[**QiskitOptimizationError**](qiskit.optimization.QiskitOptimizationError "qiskit.optimization.QiskitOptimizationError") – if the number of variables in the result differs from that of the original problem.
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</Function>
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## Attributes
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### penalty
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<Attribute id="qiskit.optimization.converters.LinearEqualityToPenalty.penalty">
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Returns the penalty factor used in conversion.
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**Return type**
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`Optional`\[`float`]
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**Returns**
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The penalty factor used in conversion.
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</Attribute>
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</Class>
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