Add core symbolic gradient support to Pyomo.DoE#3928
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snarasi2 wants to merge 38 commits intoPyomo:mainfrom
Open
Add core symbolic gradient support to Pyomo.DoE#3928snarasi2 wants to merge 38 commits intoPyomo:mainfrom
snarasi2 wants to merge 38 commits intoPyomo:mainfrom
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added 30 commits
April 1, 2026 14:20
Add schema, filtering, and error-path tests for factorial-result tables and DataFrame input handling. Validation: - 101 passed, 5 warnings, 10 subtests passed in 30.00s Notes: - plotting tests emit expected matplotlib warnings under the non-interactive Agg backend because draw_factorial_figure() calls plt.show().
Add a determinant-based reactor regression test, guard Codecov uploads when coverage.xml is absent, and align local formatting with Black expectations. Validation: - focused reactor regression subset: 2 passed, 28 deselected in 10.08s - full DoE suite: 102 passed, 5 warnings, 10 subtests passed in 30.29s Notes: - plotting-related tests continue to emit expected matplotlib warnings under the non-interactive Agg backend because draw_factorial_figure() calls plt.show().
Add coverage for draw_factorial_figure guard branches covering more-than-two sensitivity variables and missing fixed design variables. Validation: - focused plotting-guard subset: 2 passed, 37 deselected in 1.92s - full DoE suite: 104 passed, 5 warnings, 10 subtests passed in 31.47s Notes: - plotting-related tests continue to emit expected matplotlib warnings under the non-interactive Agg backend because draw_factorial_figure() calls plt.show().
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@adowling2 @slilonfe5 @smondal13 @sscini This is the new symbolic PR (created from the unpolluted branch) |
Author
sscini
moved this from Todo
to Ready for design review
in ParmEst & Pyomo.DoE Development
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Fixes # .
This PR ports the core symbolic-gradient functionality from the historical pyomo-doe-symbolic work into the current pyomo.contrib.doe implementation.
Summary/Motivation:
Rather than merging the old branch directly, this change transplants the symbolic DoE pieces onto current main so that symbolic sensitivities work with the newer DoE implementation already present in Pyomo, including the current objective and GreyBox-oriented code paths.
This PR ports the core symbolic-gradient functionality from adowling2#7 onto current Pyomo main and adapts it to the current pyomo.contrib.doe implementation.
This contribution was prepared with coding assistance from OpenAI Codex. All design decisions, validation, testing, and quality-assurance responsibility remain with Shilpa Narasimhan.
Changes proposed in this PR:
GradientMethodsupport toDesignOfExperimentsExperimentGradientsfrompyomo.contrib.doeExperimentGradientsso symbolic and automatic differentiation are set up togetherrun_doe()from being called withGradientMethod.kaugcyipopt/ MA57-HSL test behavior where relevantValidation performed locally:
python -m black -S -C --check --diff pyomo/contrib/doe→ passedtypos --config .github/workflows/typos.toml pyomo/contrib/doe DOE_SYMBOLIC_PR_NOTES.md→ passed134 passed, 0 failed, 0 skippedcyipopttests were run with MA57/HSL available in the local environmentLegal Acknowledgement
By contributing to this software project, I have read the contribution guide and agree to the following terms and conditions for my contribution: