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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

@marko1olo marko1olo commented 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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@murrayrm

murrayrm commented Jun 7, 2026

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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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@marko1olo

marko1olo commented Jun 7, 2026

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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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@marko1olo

marko1olo commented Jun 7, 2026

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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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@slivingston slivingston mentioned this pull request Jun 17, 2026
Add dlqe filter-form gain option #1220
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@murrayrm

murrayrm commented Aug 9, 2026

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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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@murrayrm murrayrm mentioned this pull request Aug 9, 2026
Test norm methods slycot and scipy separately #1201
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@marko1olo
docs: clarify lqe and dlqe error covariance wording
516ba2e
@coveralls

coveralls commented Aug 13, 2026

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Coverage Status

coverage: 94.743%. remained the same — marko1olo:docs-lqe-dlqe-error-covariance into python-control:main

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@murrayrm
murrayrm merged commit 559692e into python-control:main Aug 14, 2026
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Inaccurate wording in documentation of lqe and dlqe

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@marko1olo @murrayrm @coveralls
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