GeezerVison is showing github.com — docs: clarify lqe and dlqe error covariance wording by marko1olo · Pull Request #1219 · python-control/python-control
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marko1olo
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Jun 5, 2026
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Closes #1183.
This updates the lqe and dlqe docstrings 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.py
python -m pytest control/tests/stochsys_test.py -q -> 25 passed, 2 skipped
python -m numpydoc render control.stochsys.lqe
python -m numpydoc render control.stochsys.dlqe
$env:PYTHONPATH='..'; python -m pytest test_sphinxdocs.py::test_config_defaults -q -> 1 passed, 4 warnings
Known unrelated local docs issues:
python -m numpydoc validate control.stochsys.lqe/control.stochsys.dlqe reports pre-existing docstring style findings.
- The full Sphinx docs test file needs generated docs/CWD setup that is not present in this fresh clone.
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.
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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.
lqe and dlqe now say the estimator minimizes the mean squared estimation error (x - x_e / x[n] - x_e[n]) using the measurements y.
Local checks:
uv run --no-project --with ruff ruff check control/stochsys.py
python -m compileall -q control\stochsys.py
git diff --check
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1 similar comment
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Thanks, updated this to avoid describing covariance as the scalar objective.
lqe and dlqe now say the estimator minimizes the mean squared estimation error (x - x_e / x[n] - x_e[n]) using the measurements y.
Local checks:
uv run --no-project --with ruff ruff check control/stochsys.py
python -m compileall -q control\stochsys.py
git diff --check
|
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Comit 6666024 looks fine, but commit 90764ad seems to have introduced a lot of extraneous changes that should not be part of this PR.
Can you revert 90764ad. You may also need to wait until PR #1243 is merged into main and then rebase if unit tests don't pass.
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coverage: 94.743%. remained the same — marko1olo:docs-lqe-dlqe-error-covariance into python-control:main
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