Add Primal-Dual Interior-Point Method for linear programming #11497
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| max_iter (int): Maximum number of iterations. | ||
| """ | ||
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| def __init__(self, c: np.ndarray, a: np.ndarray, b: np.ndarray, tol: float = 1e-8, max_iter: int = 100) -> None: |
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Please provide descriptive name for the parameter: c
Please provide descriptive name for the parameter: a
Please provide descriptive name for the parameter: b
| if not self._is_valid_input(): | ||
| raise ValueError("Invalid input for the linear programming problem.") | ||
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| def _is_valid_input(self) -> bool: |
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As there is no test file in this pull request nor any test function or class in the file linear_programming/interior_point_method.py, please provide doctest for the function _is_valid_input
| """Validate the input for the linear programming problem.""" | ||
| return (self.a.shape[0] == self.b.shape[0]) and (self.a.shape[1] == self.c.shape[0]) | ||
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| def _convert_to_standard_form(self): |
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Please provide return type hint for the function: _convert_to_standard_form. If the function does not return a value, please provide the type hint as: def function() -> None:
As there is no test file in this pull request nor any test function or class in the file linear_programming/interior_point_method.py, please provide doctest for the function _convert_to_standard_form
| c_standard = np.hstack([self.c, np.zeros(m)]) | ||
| return a_standard, c_standard | ||
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| def solve(self) -> tuple[np.ndarray, float]: |
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As there is no test file in this pull request nor any test function or class in the file linear_programming/interior_point_method.py, please provide doctest for the function solve
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| if not self._is_valid_input(): | ||
| raise ValueError("Invalid input for the linear programming problem.") | ||
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| def _is_valid_input(self) -> bool: |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file linear_programming/interior_point_method.py, please provide doctest for the function _is_valid_input
| self.constraint_matrix.shape[1] == self.objective_coefficients.shape[0] | ||
| ) | ||
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| def _convert_to_standard_form(self) -> tuple[np.ndarray, np.ndarray]: |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file linear_programming/interior_point_method.py, please provide doctest for the function _convert_to_standard_form
| c_standard = np.hstack([self.objective_coefficients, np.zeros(m)]) | ||
| return a_standard, c_standard | ||
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| def solve(self) -> tuple[np.ndarray, float]: |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file linear_programming/interior_point_method.py, please provide doctest for the function solve
for more information, see https://pre-commit.ci
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| if not self._is_valid_input(): | ||
| raise ValueError("Invalid input for the linear programming problem.") | ||
|
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||
| def _is_valid_input(self) -> bool: |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file linear_programming/interior_point_method.py, please provide doctest for the function _is_valid_input
| and self.constraint_matrix.shape[1] == self.objective_coefficients.shape[0] | ||
| ) | ||
|
|
||
| def _convert_to_standard_form(self) -> tuple[np.ndarray, np.ndarray]: |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file linear_programming/interior_point_method.py, please provide doctest for the function _convert_to_standard_form
| c_standard = np.hstack([self.objective_coefficients, np.zeros(m)]) | ||
| return a_standard, c_standard | ||
|
|
||
| def solve(self) -> tuple[np.ndarray, float]: |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file linear_programming/interior_point_method.py, please provide doctest for the function solve
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| if not self._is_valid_input(): | ||
| raise ValueError("Invalid input for the linear programming problem.") | ||
|
|
||
| def _is_valid_input(self) -> bool: |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file linear_programming/interior_point_method.py, please provide doctest for the function _is_valid_input
| and self.constraint_matrix.shape[1] == self.objective_coefficients.shape[0] | ||
| ) | ||
|
|
||
| def _convert_to_standard_form(self) -> tuple[np.ndarray, np.ndarray]: |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file linear_programming/interior_point_method.py, please provide doctest for the function _convert_to_standard_form
| c_standard = np.hstack([self.objective_coefficients, np.zeros(m)]) | ||
| return a_standard, c_standard | ||
|
|
||
| def solve(self) -> tuple[np.ndarray, float]: |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file linear_programming/interior_point_method.py, please provide doctest for the function solve
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Closing tests_are_failing PRs to prepare for Hacktoberfest 2024 |
Describe your change:
Checklist:
Description of the change:
This pull request adds an implementation of the Primal-Dual Interior-Point Method for solving linear programming problems. The method handles
>=,<=, and=constraints and ensures each variablex1, x2, ... >= 0.Changes:
InteriorPointMethodclass to solve linear programming problems using the Primal-Dual Interior-Point Method.Testing:
References: