perf: use single-step sparse matrix slicing in MatrixAccessor#618
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MaykThewessen wants to merge 1 commit intoPyPSA:masterfrom
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perf: use single-step sparse matrix slicing in MatrixAccessor#618MaykThewessen wants to merge 1 commit intoPyPSA:masterfrom
MaykThewessen wants to merge 1 commit intoPyPSA:masterfrom
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Replace double-slicing pattern A[clabels][:, vlabels] with single-step A[np.ix_(clabels, vlabels)] in MatrixAccessor.A and MatrixAccessor.Q. The double-slice creates an intermediate sparse matrix (selecting rows first, then columns), which allocates temporary storage proportional to the full matrix. np.ix_() performs both row and column selection in a single operation, avoiding the intermediate allocation. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Summary
Replace the double-slicing pattern in
MatrixAccessor.AandMatrixAccessor.Qwith single-stepnp.ix_()indexing.Before:
After:
Motivation
The double-slice
A[rows][:, cols]creates an intermediate sparse matrix after the first slice, then slices again.np.ix_()expresses the row+column selection as a single operation, avoiding the intermediate allocation. For large constraint matrices (~1.38M rows × ~593K cols), this reduces memory churn.Context
See #198 (comment) — item 4 in the priority list.
Test plan
test_matrices.py— all 4 tests pass (shape validation, masked models, duplicated variables, float coefficients)test_io.py::test_to_highspy— passestest_optimization.pyhighs-direct — 24/25 pass (one pre-existing failure)🤖 Generated with Claude Code