add cartpole example#10
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Enable cp.Parameter objects to be treated as updatable nodes in the C expression DAG instead of being baked in as constants. On re-solve with new parameter values, the cached DAG and sparsity structures are reused via problem._nlp_cache, analogous to DPP for conic programs. Requires SparseDiffPy PR #10 for make_parameter, problem_register_params, and problem_update_params bindings. Tests skip gracefully until installed. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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* Add parameter support to DNLP diff engine Enable cp.Parameter objects to be treated as updatable nodes in the C expression DAG instead of being baked in as constants. On re-solve with new parameter values, the cached DAG and sparsity structures are reused via problem._nlp_cache, analogous to DPP for conic programs. Requires SparseDiffPy PR #10 for make_parameter, problem_register_params, and problem_update_params bindings. Tests skip gracefully until installed. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Remove unused converters and simplify NLP parameter cache Remove diag_mat, upper_tri, vstack, and kron converters and their tests. Simplify solve_nlp parameter caching to mirror the best_of pattern: always re-run the full reduction chain, cache only the solver_cache (Oracles) on problem._nlp_cache between solve() calls. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Simplify parameter plumbing: replace InverseData with param_id_map Replace build_theta(params, inverse_data) with simple concatenation. Add build_param_id_map() helper. Replace inverse_data parameter with param_id_map dict throughout converters, C_problem, and Oracles. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Remove test_nlp_parameters.py for now Will revisit testing after SparseDiffPy parameter bindings are available. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Clean up converters: add ConvertContext, uniform converter signatures Replace scattered var_dict/n_vars/param_id_map threading with a single ConvertContext class built from InverseData. All atom converters now have uniform (expr, children, ctx) signature. Remove _is_parametric, _PARAMETRIC_CONVERTERS, build_param_id_map, build_variable_dict, convert_expressions. C_problem creates its own InverseData internally. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Keep original (expr, children) converter signatures Move matmul/multiply parameter handling to ConvertContext methods. All other converters keep their original (expr, children) signature untouched. convert_expr dispatches matmul/multiply specially via ctx. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Restore original converter signatures, minimal parameter changes Keep all atom converters with their original (expr, children) signatures. Only matmul and multiply are handled via ConvertContext methods. ATOM_CONVERTERS dict entries preserved exactly as original. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Move parameter update into solve_via_data, use _solver_cache['NLP'] Move update_params call into ipopt solve_via_data (where oracles are reused). Remove _get_solver_cache helper and _nlp_cache dict. Store solver_cache in the existing problem._solver_cache['NLP'] — zero changes to problem.py. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Clean up parameter update: inline into Oracles, add to all NLP solvers Move theta construction into Oracles.update_params(problem). Remove build_theta from converters. Add update_params call to all four NLP solvers (IPOPT, Knitro, Uno, COPT) on oracle reuse path. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * adds tests for parameters * Add parameter tests, initial value init, update_params in all solvers - Add test_nlp_parameters.py with 3 tests (scalar, vector, matrix) - Initialize parameter values after registration in C_problem - Add oracles.update_params() call to all NLP solvers on cache reuse - Use problem._solver_cache['NLP'] for oracle caching - Restore original docstrings in c_problem.py Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Always pass param_node to matmul helpers (required by new SparseDiffPy) The parameter-bindings branch of SparseDiffPy always requires param_or_none as the first arg to matmul functions. Pass it unconditionally (None for non-parametric). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Replace ConvertContext with flat functions for converter state Keep master's convert_expr(expr, var_dict, n_vars) pattern, adding param_dict as an optional arg. Matmul/multiply stay as standalone functions rather than class methods. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Update CLAUDE.md with DNLP architecture documentation Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Split converters.py into helpers.py, registry.py, and converters.py - helpers.py: shared utilities, matmul helpers, var/param dict builders - registry.py: atom converter functions and ATOM_CONVERTERS dict - converters.py: convert_expr entry point and param-aware matmul/multiply Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Fix lint and remove section comment banners Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * chnages from code review with dance * add test for right elementwise multiply, which forces an argument order swap * minor edits to parameters * adding test for parametrized least-squares vs. clarabel * make three more legit parameter tests * tests * fix tests so they test the right thing * added comment * add comments * add comment * small tweaks to pass parameter values and new tests * add derivative checker to parameter tests * two more tests * trigger ci rerun * update pinned sparsediffpy version to 0.2.0 --------- Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com> Co-authored-by: dance858 <danielcederberg1@gmail.com>
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suggested by kevin tracy.
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