sync from master #135
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* WIP - I have two questions in NOTES - I want to add a small test to verify that the variable names do appear in the exported LP files - I need to remove the lines that export the LPs * Refactor logic into a collect_column_names function and add tests * make linter happy * Fix HiGHS LP export test for Windows * Remove accidental breakpoint() from CuClarabel test * Cleanup HiGHS write_model_file implementation - Fix type hint: validate_column_name returns None, not bool - Extract set_column_names_from_variables() helper to reduce duplication - Remove outdated TODO about HiGHS PR #2274 (merged in v1.11.0) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> --------- Co-authored-by: Halil Sen <20163961+senhalil@users.noreply.github.com> Co-authored-by: Steven Diamond <diamond@cs.stanford.edu> Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
* feat: support warm_start for COPT QP extension * feat: add copt warmstart test
* first prototype of nd matmul * cleaning up and adding tests * more and cleaner ND matmul tests * unified code paths * remove kron_r * further unification of methods * make COO matmul clearer * add unit tests and optimize more cases * align scipy and coo * tests + broadcasting * clarify cases for mul backend * all tests pass * clarification / cleanup * attempt at cleanup * clarify get constant data * fix failing tests * distinguish reshape use cases * reorganized new tests * remove unneeded comments * remove erroneous test * Add comprehensive docstrings for COO backend ND-matmul operations Improve documentation for key functions in the COO canonicalization backend: - `select_rows`: Explain broadcast deduplication and param_idx handling - `_select_rows_no_duplicates`: Add concrete example of reverse mapping - `_select_rows_with_duplicates`: Detailed algorithm walkthrough with example - `_kron_nd_structure`: Explain why I_n ⊗ C ⊗ I_B structure is needed for batched matmul, with full derivation and visual example - `_build_interleaved`: Explain batch-varying constant handling with interleaved indexing pattern and concrete matrix example - `mul`: Document the three cases (parametric, batch-varying, 2D constant) and why each requires different handling - `broadcast_to`, `promote`: Explain how they create duplicate row indices Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * Add detailed docstring for reshape_parametric_constant Document the broadcast deduplication logic: - Explain why duplicates occur (broadcast via select_rows) - Show what goes wrong without deduplication - Concrete example: P(2,3) broadcast to (4,2,3) showing before/after - Explain param_idx to (row, col) position calculation Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * Address review comments on PR #3048 - Add cross-reference from select_rows to broadcast_to/reshape_parametric_constant - Add assertion that batch-varying constants are non-parametric - Add detection logic section to mul() docstring - Clarify comment about p in scipy_backend reshape - Add detailed comment explaining broadcast detection via duplicate param_idx Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> --------- Co-authored-by: William Zijie Zhang <william@gridmatic.com> Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
Co-authored-by: Steven Diamond <steven@optimalintellect.com>
* iterating on moreau interface * fix pcp tests * fixed tests * Add MOREAU to CITATION_DICT and fix missing solver imports - Add MOREAU citation to centralized CITATION_DICT - Update moreau_conif.py to use CITATION_DICT instead of hardcoded citation - Add missing solver imports to conic_solvers/__init__.py: COSMO, CUCLARABEL, CUOPT, ECOS_BB, MOREAU - Sort imports alphabetically for consistency Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * change url * unneeded special handling for IPM settings * docs * back to old table * remove unnecesssary soc3 changes * ruff fixes * fix soc dim 3 * dimension check * address comments --------- Co-authored-by: Steven Diamond <steven@optimalintellect.com> Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
* Complete trace TODO in sparse covariance estimate example * Shift from trace to vdot for speed improvement * Add explanation for use of vdot
Add a dismissable banner to the documentation announcing: - The inaugural CVXPY Community Workshop on Feb 20-21 at Stanford/Zoom - Listening sessions for community feedback Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
* add ND rmul backend support * remove extra copy * round of review * working on aligning mul and rmul * align mul and rmul * fix: handle 1D vectors correctly in get_nd_rmul_dims For 1D vectors in rmul (w @ alpha where both are 1D): - Variable w is (k,) treated as row vector (1, k), so m=1 - Constant alpha is (k,) treated as column vector (k, 1), so n=1 The previous code incorrectly used var_shape[0] for m and const_shape[-1] for n, which caused dimension mismatches like (3, 9) @ (3, 3). Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * test: add regression test for 1D vector @ 1D constant Test the case where both variable and constant are 1D vectors, which produces a scalar result. This catches the bug fixed in the previous commit where get_nd_rmul_dims incorrectly computed m and n for 1D vectors. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
Bumps [JamesIves/github-pages-deploy-action](https://github.com/jamesives/github-pages-deploy-action) from 4.7.6 to 4.8.0. - [Release notes](https://github.com/jamesives/github-pages-deploy-action/releases) - [Commits](JamesIves/github-pages-deploy-action@v4.7.6...v4.8.0) --- updated-dependencies: - dependency-name: JamesIves/github-pages-deploy-action dependency-version: 4.8.0 dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
The moreau solver API has been updated to remove the `l` and `u` variable bounds parameters. Variable bounds are now handled through CVXPY's standard constraint mechanism via the CvxAttr2Constr reduction. Changes: - Set BOUNDED_VARIABLES = False to use constraint-based bounds - Remove bounds extraction and passing in solve_via_data() Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
* Add num_iters to gurobi conic solver * Use hasattr * ruff * Address comment
The SOCDim3 reduction failed with IndexError when X was a scalar (shape ()) because len(X.shape) == 0 was not handled. Changed the condition from `len(X.shape) == 1` to `len(X.shape) <= 1` to handle both scalar and 1D cases. Added regression test test_scalar_x_soc that verifies scalar SOC constraints work correctly through the decomposition. Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
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