tighter test checks

[aadityacs]

No description provided.

[gemini-code-assist]

Summary of Changes

Hello @aadityacs, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request focuses on enhancing the precision and reliability of the test suite by implementing tighter numerical checks. It systematically reduces the tolerance values in several key assertions within mmapy/mma_test.py, ensuring that the tested components adhere more closely to their expected mathematical properties and behaviors. This change aims to improve the overall quality and robustness of the codebase by catching more subtle deviations in calculations.

Highlights

• Stricter Numerical Tolerances: Reduced acceptable error margins across various numerical assertion checks to improve test precision.
• Constraint and Multiplier Checks: Tightened the tolerance for constraint activity from "0.1" to "0.05" and the minimum multiplier value from "0.1" to "0.01".
• Stationarity Condition Checks: Significantly decreased the residual norm tolerance for stationarity at both lower and upper bounds from "0.5" to "0.01".
• Scaling Relationship Accuracy: Improved the accuracy requirement for multiplier scaling with the objective and the constancy of "lambda * scale" by reducing relative error tolerances from "20-30%" to "1%".

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[gemini-code-assist]

Code Review

This pull request tightens the tolerances in several tests, making them more precise, which is a great improvement. I've also suggested a couple of minor refactorings to use np.testing.assert_allclose for floating-point comparisons. This is more idiomatic when using NumPy and improves the readability and robustness of the tests.

[gemini-code-assist]

While the manual calculation of relative error works, using np.testing.assert_allclose is more idiomatic and robust for comparing floating-point numbers in NumPy-based tests. It clearly expresses the intent of checking for approximate equality with a relative tolerance and handles edge cases around zero more gracefully.

Suggested change

	relative_error = abs(actual_lambda - expected_lambda) / max(expected_lambda, 1e-6)
	self.assertLess(
	relative_error,
	0.2, # 20% tolerance
	0.01, # 1% tolerance
	msg=f"Multiplier should scale with objective. "
	f"Expected {expected_lambda:.4f}, got {actual_lambda:.4f}",
	)
	np.testing.assert_allclose(
	actual_lambda,
	expected_lambda,
	rtol=0.01, # 1% tolerance
	err_msg=(
	"Multiplier should scale with objective. "
	f"Expected {expected_lambda:.4f}, got {actual_lambda:.4f}"
	),
	)

[gemini-code-assist]

For improved readability and to follow common testing patterns with NumPy, consider using np.testing.assert_allclose. This function is designed for comparing floating-point values and makes the intent of a relative tolerance check clearer than manually calculating the deviation.

Suggested change

	relative_deviation = abs(prod - mean_product) / mean_product
	self.assertLess(
	relative_deviation,
	0.3, # 30% tolerance
	0.01, # 1% tolerance
	msg="λ * scale should be approximately constant",
	)
	np.testing.assert_allclose(
	prod,
	mean_product,
	rtol=0.01, # 1% tolerance
	err_msg="λ * scale should be approximately constant",
	)