Add core symbolic gradient support to Pyomo.DoE#3898
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snarasi2 wants to merge 12 commits intoPyomo:mainfrom
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Add core symbolic gradient support to Pyomo.DoE#3898snarasi2 wants to merge 12 commits intoPyomo:mainfrom
snarasi2 wants to merge 12 commits intoPyomo:mainfrom
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This is a port of adowling2#7 |
added 10 commits
April 1, 2026 16:30
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().
Author
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Outstanding item from the historical symbolic branch still remain to be completed:
Closed out:
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Author
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@sscini @smondal13 @slilonfe5 ready for preliminary review. Could you please take a look when you are free? |
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Fixes # .
Summary/Motivation:
This PR ports the core symbolic-gradient functionality from the historical pyomo-doe-symbolic work into the current pyomo.contrib.doe implementation.
Summary/Motivation:
This PR ports the core symbolic-gradient functionality from the historical
pyomo-doe-symbolicwork into the currentpyomo.contrib.doeimplementation.Rather than merging the old branch directly, this change transplants the symbolic DoE pieces onto current
mainso that symbolic sensitivities work with the newer DoE implementation already present in Pyomo, including the current objective and GreyBox-oriented code paths.This PR is intentionally scoped to the core symbolic-gradient integration. It does not attempt to port every example, test refactor, or auxiliary change from the historical
adowling2/pyomo-doe-symbolicbranch in one step.This contribution was prepared with coding assistance from OpenAI Codex. All design decisions, validation, testing, and quality-assurance responsibility remain with Shilpa Narasimhan.
Local validation included:
pyomo/contrib/doe/tests:133 passed, 4 skipped, 5 warnings, 10 subtests passedpyomo/contrib/doe/tests/test_greybox.py:33 passedin HSL-enabled local environmentsThe remaining public-CI GreyBox
cyipoptskips are environment-dependent and tied to MA57/HSL runtime availability.Changes proposed in this PR:
GradientMethodsupport toDesignOfExperimentspynumerogradient path for DoEExperimentGradientsfrompyomo.contrib.doeExperimentGradientsso symbolic and automatic differentiation are set up togetherrun_doe()from being called withGradientMethod.kaugpolynomialexample and polynomial-focused regression coveragecyipopttest skip reason when the MA57/HSL runtime is unavailableLegal Acknowledgement
By contributing to this software project, I have read the contribution guide and agree to the following terms and conditions for my contribution: