docs: clarify lqe and dlqe error covariance wording - #1219
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I don't think that the new wording is accurate. In particular, it uses the term "minimum covariance", but the covariance is (in general) a matrix and so the minimum depends on the norm that you use (eg, determinant or trace). As noted in #1183, I think that something like "minimizes the mean squared error" or perhaps "minimizes the mean of x - x_e" would be more accurate. |
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Thanks, updated this to avoid describing covariance as the scalar objective.
Local checks:
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Thanks, updated this to avoid describing covariance as the scalar objective.
Local checks:
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coverage: 94.743%. remained the same — marko1olo:docs-lqe-dlqe-error-covariance into python-control:main |
Closes #1183.
This updates the
lqeanddlqedocstrings to describe the steady-state estimation error covariance minimized by the estimator, instead of saying only that the state estimate minimizes expected squared error.I avoided adding a runtime behavior change or changing generated docs directly.
Validation:
python -m ruff check control/stochsys.pypython -m pytest control/tests/stochsys_test.py -q-> 25 passed, 2 skippedpython -m numpydoc render control.stochsys.lqepython -m numpydoc render control.stochsys.dlqe$env:PYTHONPATH='..'; python -m pytest test_sphinxdocs.py::test_config_defaults -q-> 1 passed, 4 warningsKnown unrelated local docs issues:
python -m numpydoc validate control.stochsys.lqe/control.stochsys.dlqereports pre-existing docstring style findings.AI Disclosure: Codex (ChatGPT 5.5) was used during code navigation, initial drafting, and PR text preparation. All logic, code changes, and test cases have been manually reviewed, verified, and tested locally by the author in accordance with the NumPy AI Policy.