Support indicator, SOS and semi-continuous constraints in SCIP

[mcoughlin]

SCIP was already reading indicator constraints, SOS constraints and semi-continuous variables; now it actually uses them.

Note

Code was checked by AI (Claude Code).

Changes proposed in this Pull Request

Plugs in SCIP indicator constraints, SOS constraints and semi-continuous variables such that they are used in the solver.

Checklist

• AI-generated content is marked (see AGENTS.md).
• Code changes are sufficiently documented; i.e. new functions contain docstrings and further explanations may be given in doc.
• Unit tests for new features were added (if applicable).
• A note for the release notes doc/release_notes.rst of the upcoming release is included.
• I consent to the release of this PR's code under the MIT license.

[codspeed-hq]

Merging this PR will improve performance by 31.55%

⚠️ Different runtime environments detected

Some benchmarks with significant performance changes were compared across different runtime environments,
which may affect the accuracy of the results.

Open the report in CodSpeed to investigate

⚡ 5 improved benchmarks
✅ 176 untouched benchmarks
⏩ 181 skipped benchmarks¹

Performance Changes

	Benchmark	BASE	HEAD	Efficiency
⚡	test_to_lp[sparse_network-n=10]	1,343.1 KB	718.1 KB	+87.03%
⚡	test_to_lp[qp-n=1000]	2.6 MB	2 MB	+29.19%
⚡	test_to_lp[storage-n=250]	37 MB	30.1 MB	+22.77%
⚡	test_to_lp[nodal_balance-severity=0]	3.7 MB	3.1 MB	+19.52%
⚡	test_to_lp[nodal_balance_sparse-severity=0]	3.7 MB	3.3 MB	+11.14%

Tip

Curious why performance improved? Comment @codspeedbot explain why performance improved on this PR, or directly use the CodSpeed MCP with your agent.

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_{Comparing mcoughlin:finish-scip (fffd996) with master (96dd488)}

Footnotes

1. 181 benchmarks were skipped, so the baseline results were used instead. If they were deleted from the codebase, click here and archive them to remove them from the performance reports. ↩