perf: cache MatrixAccessor properties to avoid redundant recomputation

[MaykThewessen]

Summary

• Change vlabels, clabels, A, c, b, sense, lb, ub, vtypes, and Q from @property to @cached_property in MatrixAccessor
• Add all new cached properties to clean_cached_properties() for proper invalidation
• Add test verifying caching behavior and cache invalidation

Motivation

During to_highspy() (and other direct API exporters), MatrixAccessor properties are accessed multiple times — both directly and indirectly. For example, M.A internally accesses M.clabels and M.vlabels, and M.c accesses M.flat_vars. Each access recomputes from scratch, including rebuilding sparse matrices and flattening DataFrames.

For large models (~593K variables, ~1.38M constraints), this redundant recomputation adds measurable overhead per solve call. Caching eliminates this since the underlying model data doesn't change between property accesses within a single solve.

The cache is already properly invalidated at the start of Model.solve() and Model._mock_solve() via existing clean_cached_properties() calls.

Context

See discussion in #198 (comment) for profiling data from a real-world 52-chunk DC-OPF optimization.

Test plan

• Existing test_matrices.py tests pass (verify shapes, values, masked models)
• New test_matrices_properties_are_cached verifies identity caching and invalidation
• test_optimization.py highs-direct tests pass (24/25 — one pre-existing failure in test_modified_model)
• test_io.py tests pass

Note: test_modified_model fails on upstream master as well — it's a pre-existing issue unrelated to this change.

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