Fix GDPopt LOA maximization objective sense

[bernalde]

Fixes #3937.

Summary/Motivation:

GDPopt LOA currently creates the augmented OA master objective with minimization sense even when the original GDP objective is a maximization objective. For maximization GDPs, this can make the discrete OA master pursue the wrong objective direction.

This was observed while validating the GDPLib methanol benchmark:

• Track GDPopt LOA objective-sense discrepancy SECQUOIA/gdplib#114 tracks the downstream methanol LOA mismatch.
• Fix methanol GDPopt solve options SECQUOIA/gdplib#113 documents and preserves the downstream sign-convention workaround.

In the methanol reproducer, the algebraically equivalent formulations minimize(-profit) and maximize(profit) selected different disjuncts before this fix. With this branch, both formulations return the same profit and active disjuncts.

Changes proposed in this PR:

• Preserve the original objective sense when GDPopt LOA creates the augmented OA master objective.
• Keep the placeholder OA objective expressionless until the augmented expression is assigned.
• Add regression coverage for a small GDP where minimize(-x) and maximize(x) should select the same disjunct.

Tests run:

• python -m pytest -q pyomo/contrib/gdpopt/tests/test_gdpopt.py::TestGDPoptUnit::test_loa_augmented_penalty_objective_preserves_objective_sense pyomo/contrib/gdpopt/tests/test_gdpopt.py::TestGDPoptUnit::test_LOA_maximize_matches_minimize_negated_objective
  • Result: 2 passed in 1.40s
• python -m pytest -q pyomo/contrib/gdpopt/tests/test_gdpopt.py
  • Result: 60 passed, 21 skipped, 3 deselected in 16.46s
• python -m black --check --diff pyomo/contrib/gdpopt/loa.py pyomo/contrib/gdpopt/tests/test_gdpopt.py
  • Result: passed, 2 files would be left unchanged

Legal Acknowledgement

By contributing to this software project, I have read the contribution guide and agree to the following terms and conditions for my contribution:

1. I agree my contributions are submitted under the BSD license.
2. I represent I am authorized to make the contributions and grant the license. If my employer has rights to intellectual property that includes these contributions, I represent that I have received permission to make contributions and grant the required license on behalf of that employer.

[bernalde]

Verified the fix against the GDPLib methanol instance that motivated #3937.

Setup:

• Pyomo branch: fix/issue-3937-loa-maximize
• GDPLib source: SECQUOIA/gdplib main, imported with PYTHONPATH=/tmp/gdplib-methanol-3937
• GDPopt call: algorithm="LOA", mip_solver="gams", nlp_solver="gams"

Results:

• minimize(-profit): optimal, profit 1793.4292381783353, 15 iterations
• maximize(profit): optimal, profit 1793.4292381783353, 15 iterations

Both formulations selected the same active disjuncts:

cheap_reactor
expensive_feed_disjunct
single_stage_recycle_compressor_disjunct
two_stage_feed_compressor_disjunct

This directly addresses the original failure mode: the maximization form no longer terminates at the negative-profit local solution and now matches the minimized negative-profit formulation for this benchmark.

[bernalde]

I would approve this review, but GitHub does not allow the authenticated author account to approve its own pull request.

No blocking issues found.

Reviewed the LOA objective construction, the objective-sense-aware bound update path, the new focused unit/regression tests, and the repository test conventions. The change is narrowly scoped and preserves the existing penalty expression behavior while ensuring the OA master objective has the same sense as the original objective.

Tests run:

• python -m pytest -q pyomo/contrib/gdpopt/tests/test_gdpopt.py::TestGDPoptUnit::test_loa_augmented_penalty_objective_preserves_objective_sense pyomo/contrib/gdpopt/tests/test_gdpopt.py::TestGDPoptUnit::test_LOA_maximize_matches_minimize_negated_objective - 2 passed
• python -m pytest -q pyomo/contrib/gdpopt/tests/test_gdpopt.py - 62 passed, 19 skipped, 3 deselected
• python -m pytest -q pyomo/contrib/gdpopt/tests - 76 passed, 35 skipped, 5 deselected
• python -m black --check --diff pyomo/contrib/gdpopt/loa.py pyomo/contrib/gdpopt/tests/test_gdpopt.py - passed, files unchanged
• GDPLib methanol reproducer with algorithm="LOA", mip_solver="gams", nlp_solver="gams" - both minimize(-profit) and maximize(profit) returned optimal profit 1793.4292381783353 in 15 iterations with the same active disjuncts

Merge-ready from this review.

[emma58]

This is kind of an embarrassing bug--thanks for catching it! A couple comments below, though neither is major.

[emma58]

No need to set an expr here since it's a placeholder.

[bernalde]

Addressed in 3262583. The OA placeholder is now expressionless, and its sense is assigned explicitly after construction so the later augmented objective keeps the original sense.

[emma58]

I'm not a big fan of using private methods in tests since it makes for a lot ofediting if/when they change, but there may not be a great way around this one.

[bernalde]

Not changed. I kept this focused unit test because it directly isolates the objective-sense setup, while the adjacent public LOA solve regression still covers the behavior through solve(). I also reran the public path manually with Gurobi after the commit.

[bernalde]

Addressed the current PR review comments.

Commits pushed:

• 326258353c04f81ad49d31add5b954069fb13901 Remove dummy LOA objective expression

Main changes made:

• Replaced the dummy expr=0 OA objective placeholder with an expressionless Objective().
• Set the placeholder objective sense explicitly after construction so the later augmented objective still preserves the original objective sense.

Tests run:

• conda run -n pyomo-local-test python -m pytest -q pyomo/contrib/gdpopt/tests/test_gdpopt.py::TestGDPoptUnit::test_loa_augmented_penalty_objective_preserves_objective_sense pyomo/contrib/gdpopt/tests/test_gdpopt.py::TestGDPoptUnit::test_LOA_maximize_matches_minimize_negated_objective -> 1 passed, 1 skipped. The solve regression is skipped by the test guard because the configured test MIP solver is unavailable in this env.
• conda run -n pyomo-local-test python -m pytest -q pyomo/contrib/gdpopt/tests/test_gdpopt.py -> 7 passed, 74 skipped, 3 deselected.
• conda run -n pyomo-local-test python -m black --check --diff pyomo/contrib/gdpopt/loa.py pyomo/contrib/gdpopt/tests/test_gdpopt.py -> 2 files would be left unchanged.
• Manual public LOA regression with mip_solver="gurobi", nlp_solver="gurobi" for both minimize(-x) and maximize(x) -> both optimal with x = 9.

Comments intentionally not addressed:

• The private-method unit test was left in place. It is a focused unit regression for the objective-sense construction, and the adjacent public LOA solve regression still covers the behavior through solve(). The public path was also checked manually with Gurobi because the configured test MIP solver is unavailable in this env.

Remaining risks or follow-up items:

• No known remaining PR-review items. Local test coverage has many expected skips in this environment due missing optional solvers.

[mrmundt]

@bernalde - Required legal acknowledgement was deleted and tests are failing. Please feel free to reopen with the correct template once tests are passing.

[bernalde]

I refreshed this branch against current upstream/main and updated the PR body to preserve the current Pyomo template and legal acknowledgement. GitHub would not allow this closed PR to be reopened from my account, so I opened the replacement PR here: #3986.