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---
title: LinearEqualityToPenalty
description: API reference for qiskit.optimization.converters.LinearEqualityToPenalty
in_page_toc_min_heading_level: 1
python_api_type: class
python_api_name: qiskit.optimization.converters.LinearEqualityToPenalty
---
<span id="qiskit-optimization-converters-linearequalitytopenalty" />
# qiskit.optimization.converters.LinearEqualityToPenalty
<Class id="qiskit.optimization.converters.LinearEqualityToPenalty" isDedicatedPage={true} github="https://github.com/qiskit-community/qiskit-aqua/tree/stable/0.8/qiskit/optimization/converters/linear_equality_to_penalty.py" signature="LinearEqualityToPenalty(penalty=None)" modifiers="class">
Convert a problem with only equality constraints to unconstrained with penalty terms.
**Parameters**
**penalty** (`Optional`\[`float`]) Penalty factor to scale equality constraints that are added to objective. If None is passed, penalty factor will be automatically calculated.
### \_\_init\_\_
<Function id="qiskit.optimization.converters.LinearEqualityToPenalty.__init__" signature="__init__(penalty=None)">
**Parameters**
**penalty** (`Optional`\[`float`]) Penalty factor to scale equality constraints that are added to objective. If None is passed, penalty factor will be automatically calculated.
</Function>
## Methods
| | |
| ------------------------------------------------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------- |
| [`__init__`](#qiskit.optimization.converters.LinearEqualityToPenalty.__init__ "qiskit.optimization.converters.LinearEqualityToPenalty.__init__")(\[penalty]) | **type penalty**`Optional`\[`float`] |
| [`convert`](#qiskit.optimization.converters.LinearEqualityToPenalty.convert "qiskit.optimization.converters.LinearEqualityToPenalty.convert")(problem) | Convert a problem with equality constraints into an unconstrained problem. |
| [`decode`](#qiskit.optimization.converters.LinearEqualityToPenalty.decode "qiskit.optimization.converters.LinearEqualityToPenalty.decode")(result) | DEPRECATED Decode a result into another form using the information of conversion. |
| [`encode`](#qiskit.optimization.converters.LinearEqualityToPenalty.encode "qiskit.optimization.converters.LinearEqualityToPenalty.encode")(problem) | DEPRECATED Encode a QuadraticProgram into another form and keep the information required to decode the result. |
| [`interpret`](#qiskit.optimization.converters.LinearEqualityToPenalty.interpret "qiskit.optimization.converters.LinearEqualityToPenalty.interpret")(result) | Convert the result of the converted problem back to that of the original problem |
## Attributes
| | |
| --------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------- |
| [`penalty`](#qiskit.optimization.converters.LinearEqualityToPenalty.penalty "qiskit.optimization.converters.LinearEqualityToPenalty.penalty") | Returns the penalty factor used in conversion. |
### convert
<Function id="qiskit.optimization.converters.LinearEqualityToPenalty.convert" signature="convert(problem)">
Convert a problem with equality constraints into an unconstrained problem.
**Parameters**
**problem** (`QuadraticProgram`) The problem to be solved, that does not contain inequality constraints.
**Return type**
`QuadraticProgram`
**Returns**
The converted problem, that is an unconstrained problem.
**Raises**
[**QiskitOptimizationError**](qiskit.optimization.QiskitOptimizationError "qiskit.optimization.QiskitOptimizationError") If an inequality constraint exists.
</Function>
### decode
<Function id="qiskit.optimization.converters.LinearEqualityToPenalty.decode" signature="decode(result)">
DEPRECATED Decode a result into another form using the information of conversion.
**Return type**
`OptimizationResult`
</Function>
### encode
<Function id="qiskit.optimization.converters.LinearEqualityToPenalty.encode" signature="encode(problem)">
DEPRECATED Encode a QuadraticProgram into another form and keep the information required to decode the result.
**Return type**
`QuadraticProgram`
</Function>
### interpret
<Function id="qiskit.optimization.converters.LinearEqualityToPenalty.interpret" signature="interpret(result)">
Convert the result of the converted problem back to that of the original problem
**Parameters**
**result** (`OptimizationResult`) The result of the converted problem or the given result in case of FAILURE.
**Return type**
`OptimizationResult`
**Returns**
The result of the original problem.
**Raises**
[**QiskitOptimizationError**](qiskit.optimization.QiskitOptimizationError "qiskit.optimization.QiskitOptimizationError") if the number of variables in the result differs from that of the original problem.
</Function>
### penalty
<Attribute id="qiskit.optimization.converters.LinearEqualityToPenalty.penalty">
Returns the penalty factor used in conversion.
**Return type**
`Optional`\[`float`]
**Returns**
The penalty factor used in conversion.
</Attribute>
</Class>