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Remove Python packaging, integrate as DNLP submodule (#37)
* Remove Python packaging, add LICENSE - Remove pyproject.toml (Python packaging now handled by CVXPY) - Remove src/dnlp_diff_engine/ Python wrapper (no longer needed) - Add Apache 2.0 LICENSE file Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * Rename Python module from _core to _diffengine Update module name to _diffengine for integration with CVXPY build system. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * Update CLAUDE.md for pure C library setup Document the new workflow where Python packaging is handled by CVXPY. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * Add diag_vec atom for creating diagonal matrices from vectors Implements diag_vec which converts a vector of size n into an n×n diagonal matrix. Includes forward pass, Jacobian, and Hessian computations. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * Clean up diag_vec jacobian_init and eval_jacobian - Use standard CSR building pattern (J->p[row] = nnz) - Use next_diag counter instead of checking row == child_row * (n+1) - Simplify eval_jacobian to O(n) loop computing out_row directly Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * update readme * added our names to license --------- Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com> Co-authored-by: Daniel <danielcederberg1@gmail.com>
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‎CLAUDE.md‎

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## Overview
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DNLP-diff-engine is a C library with Python bindings that provides automatic differentiation for nonlinear optimization problems. It builds expression trees (ASTs) from CVXPY problems and computes first and second derivatives (gradients, Jacobians, Hessians) needed by NLP solvers like IPOPT.
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DNLP-diff-engine is a pure C library that provides automatic differentiation for nonlinear optimization problems. It builds expression trees (ASTs) from CVXPY problems and computes first and second derivatives (gradients, Jacobians, Hessians) needed by NLP solvers like IPOPT.
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## Build Commands
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### Python Package (Recommended)
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```bash
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# Install in development mode with uv (recommended)
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uv pip install -e ".[test]"
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# Or with pip
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pip install -e ".[test]"
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**Note:** This library is designed to be used as a git submodule in CVXPY. Python packaging is handled by the CVXPY build system, not this repository.
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# Run all Python tests (tests are in python/tests/)
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pytest
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# Run specific test file
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pytest python/tests/test_unconstrained.py
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# Run specific test
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pytest python/tests/test_unconstrained.py::test_sum_log
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# Lint with ruff
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ruff check src/
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# Auto-fix lint issues
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ruff check --fix src/
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```
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## Build Commands
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### Standalone C Library
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### Standalone C Library (for testing/development)
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```bash
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# Build core C library and tests
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./build/all_tests
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```
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### Building with CVXPY
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This library is included as a git submodule in CVXPY. To build:
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```bash
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# From the CVXPY repository root
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pip install -e . # or: uv pip install -e .
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# The _diffengine Python extension is built automatically
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```
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## Architecture
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### Expression Tree System
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### Python Bindings
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The Python package `dnlp_diff_engine` (in `src/dnlp_diff_engine/`) provides:
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**High-level API** (`__init__.py`):
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- `C_problem` class wraps the C problem struct
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- `convert_problem()` builds expression trees from CVXPY Problem objects
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- Atoms are mapped via `ATOM_CONVERTERS` dictionary (maps CVXPY atom names → converter functions)
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- Special converters handle: matrix multiplication (`_convert_matmul`), multiply with constants (`_convert_multiply`), indexing, reshape (Fortran order only)
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**Low-level C extension** (`_core` module, built from `python/bindings.c`):
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The Python C extension (`_diffengine` module, built from `python/bindings.c`) provides:
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- Atom constructors: `make_variable`, `make_constant`, `make_log`, `make_exp`, `make_add`, etc.
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- Problem interface: `make_problem`, `problem_init_derivatives`, `problem_objective_forward`, `problem_gradient`, `problem_jacobian`, `problem_hessian`
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The high-level Python API (converters, C_problem class) is in CVXPY at `cvxpy/reductions/solvers/nlp_solvers/diff_engine/`.
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### Derivative Computation Flow
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1. **Initialization**: `problem_init_derivatives()` allocates storage and computes sparsity patterns for all Jacobians and Hessians. This is done once per problem.
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- `include/` - Header files defining public API (`expr.h`, `problem.h`, atom headers)
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- `src/` - C implementation files organized by atom category
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- `src/dnlp_diff_engine/` - Python package with high-level API (`__init__.py` contains `C_problem` class and `ATOM_CONVERTERS`)
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- `python/` - Python bindings C code (`bindings.c`)
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- `python/atoms/` - Python binding headers for each atom type
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- `python/problem/` - Python binding headers for problem interface
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- `python/tests/` - Python integration tests (run via pytest): `test_unconstrained.py`, `test_constrained.py`, `test_problem_native.py`
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- `python/tests/` - Python integration tests (run via pytest from CVXPY)
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- `tests/` - C tests using minunit framework
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- `tests/forward_pass/` - Forward evaluation tests (C)
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- `tests/jacobian_tests/` - Jacobian correctness tests (C)
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3. Implement: `forward`, `jacobian_init`, `eval_jacobian`, `eval_wsum_hess` (optional), `free_type_data` (if needed)
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4. Add Python binding header in `python/atoms/`
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5. Register in `python/bindings.c` (both include and method table)
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6. Export in `src/dnlp_diff_engine/__init__.py` `__all__` list
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7. Rebuild: `uv pip install -e .`
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8. Add tests in `tests/` (C, register in `tests/all_tests.c`) and `python/tests/` (Python)
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6. Add converter in CVXPY: `cvxpy/reductions/solvers/nlp_solvers/diff_engine/converters.py`
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7. Rebuild CVXPY: `pip install -e .`
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8. Add tests in `tests/` (C, register in `tests/all_tests.c`) and CVXPY `cvxpy/tests/nlp_tests/`
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## Known Limitations
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‎LICENSE‎

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‎README.md‎

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# DNLP Diff Engine
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# CVXPY DNLP Differentiation Engine
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A C library with Python bindings for automatic differentiation of nonlinear optimization problems. Builds expression trees from CVXPY problems and computes gradients, Jacobians, and Hessians needed by NLP solvers like IPOPT.
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This repository contains a **C implementation of the differentiation engine used by CVXPY** for its extension to [**Disciplined Nonlinear Programming (DNLP)**](https://github.com/cvxgrp/DNLP).
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## Installation
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The library provides low-level routines for computing derivatives required by nonlinear programming problems.
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The library is intended as a **backend component** and is not meant to be used directly by end users.
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### Using uv (recommended)
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```bash
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uv venv .venv
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source .venv/bin/activate
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uv pip install -e ".[test]"
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```
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### Using pip
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```bash
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python -m venv .venv
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source .venv/bin/activate
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pip install -e ".[test]"
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```
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## Running Tests
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```bash
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# first go to python folder
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# Run all tests
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pytest
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```
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## Usage
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```python
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import cvxpy as cp
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import numpy as np
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from dnlp_diff_engine import C_problem
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# Define a CVXPY problem
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x = cp.Variable(3)
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problem = cp.Problem(cp.Minimize(cp.sum(cp.log(x))))
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# Convert to C problem struct
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prob = C_problem(problem)
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prob.init_derivatives()
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# Evaluate at a point
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u = np.array([1.0, 2.0, 3.0])
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obj_val = prob.objective_forward(u)
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gradient = prob.gradient()
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print(f"Objective: {obj_val}")
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print(f"Gradient: {gradient}")
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```
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## Building the C Library (standalone)
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```bash
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cmake -B build -S .
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cmake --build build
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./build/all_tests
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```

‎include/affine.h‎

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expr *new_transpose(expr *child);
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expr *new_diag_vec(expr *child);
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#endif /* AFFINE_H */

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