Add DIFFENGINE as canonicalization backend

.github/workflows/test_nlp_solvers.yml

@@ -0,0 +1,37 @@
+name: test_nlp_solvers
+
+on:
+    pull_request:
+    push:
+        branches:
+            - master
+        tags:
+          - '*'
+jobs:
+  run_nlp_tests:
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [ubuntu-latest, macos-latest]
+    steps:
+      - uses: actions/checkout@v5
+      - uses: astral-sh/setup-uv@v7
+        with:
+          python-version: "3.12"
+          enable-cache: true
+      - name: Install IPOPT (Ubuntu)
+        if: runner.os == 'Linux'
+        run: sudo apt-get update && sudo apt-get install -y coinor-libipopt-dev liblapack-dev libblas-dev
+      - name: Install IPOPT (macOS)
+        if: runner.os == 'macOS'
+        run: brew install ipopt
+      - name: Install dependencies
+        run: |
+          uv venv
+          uv pip install -e ".[testing]"
+          uv pip install cyipopt
+      - name: Print installed solvers
+        run: uv run python -c "import cvxpy; print(cvxpy.installed_solvers())"
+      - name: Run nlp tests
+        run: uv run pytest -s -v cvxpy/tests/nlp_tests/

CLAUDE.md

@@ -1,24 +1,36 @@
+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
 # CVXPY Development Guide
 
 ## Quick Reference
 
 ### Commands
 ```bash
-# Install in development mode
+# Install in development mode (includes C++ extensions)
 pip install -e .
 
 # Install pre-commit hooks (required)
 pip install pre-commit && pre-commit install
 
+# Run linter manually
+ruff check cvxpy/ --fix
+
 # Run all tests
 pytest cvxpy/tests/
 
 # Run specific test
 pytest cvxpy/tests/test_atoms.py::TestAtoms::test_norm_inf
+
+# Run benchmarks (uses asv - airspeed velocity)
+cd benchmarks && pip install -e . && asv run
 ```
 
 ## Code Style
 
+- **Python >= 3.11** required
+- **Linter**: Ruff (enforced via pre-commit hooks; auto-fixes code but fails the commit if fixes were needed). Note: `__init__.py` files are excluded from linting.
 - **Line length**: 100 characters
 - **IMPORTANT: IMPORTS AT THE TOP** of files - circular imports are the only exception
 - **IMPORTANT:** Add Apache 2.0 license header to all new files
@@ -126,14 +138,13 @@ Check with `problem.is_dpp()`. See `cvxpy/utilities/scopes.py` for implementatio
 Location: `cvxpy/atoms/` or `cvxpy/atoms/elementwise/`
 
 ```python
-from typing import Tuple
 from cvxpy.atoms.atom import Atom
 
 class my_atom(Atom):
     def __init__(self, x) -> None:
         super().__init__(x)
 
-    def shape_from_args(self) -> Tuple[int, ...]:
+    def shape_from_args(self) -> tuple[int, ...]:
         return self.args[0].shape
 
     def sign_from_args(self) -> Tuple[bool, bool]:

cvxpy/__init__.py

@@ -109,9 +109,11 @@
     SOLVER_ERROR as SOLVER_ERROR,
     UNBOUNDED as UNBOUNDED,
     UNBOUNDED_INACCURATE as UNBOUNDED_INACCURATE,
+    UNO as UNO,
     USER_LIMIT as USER_LIMIT,
     XPRESS as XPRESS,
     HIGHS as HIGHS,
+    IPOPT as IPOPT,
     KNITRO as KNITRO,
     get_num_threads as get_num_threads,
     set_num_threads as set_num_threads,

cvxpy/atoms/__init__.py

@@ -72,6 +72,8 @@
 from cvxpy.atoms.elementwise.sqrt import sqrt
 from cvxpy.atoms.elementwise.square import square
 from cvxpy.atoms.elementwise.xexp import xexp
+from cvxpy.atoms.elementwise.trig import sin, cos, tan
+from cvxpy.atoms.elementwise.hyperbolic import sinh, asinh, tanh, atanh
 from cvxpy.atoms.eye_minus_inv import eye_minus_inv, resolvent
 from cvxpy.atoms.gen_lambda_max import gen_lambda_max
 from cvxpy.atoms.geo_mean import GeoMean, GeoMeanApprox, geo_mean
@@ -161,6 +163,12 @@
     ptp
 ]
 
+NON_SMOOTH_ATOMS = [
+    abs,
+    maximum,
+    minimum,
+]
+
 # DGP atoms whose Dgp2Dcp canonicalization produces ExpCone-requiring DCP atoms.
 GP_EXP_ATOMS = [
     AddExpression,

cvxpy/atoms/affine/affine_atom.py

@@ -57,6 +57,10 @@ def is_atom_concave(self) -> bool:
         """
         return True
 
+    def is_atom_smooth(self) -> bool:
+        """Is the atom smooth?"""
+        return True
+
     def is_incr(self, idx) -> bool:
         """Is the composition non-decreasing in argument idx?
         """

cvxpy/atoms/affine/transpose.py

@@ -116,6 +116,7 @@ def graph_implementation(
         """
         return (lu.transpose(arg_objs[0], self.axes), [])
 
+
 def permute_dims(expr, axes: List[int]):
     """Permute the dimensions of the expression.
 

cvxpy/atoms/atom.py

@@ -182,12 +182,16 @@ def is_atom_concave(self) -> bool:
         """Is the atom concave?
         """
         raise NotImplementedError()
-
+    
     def is_atom_affine(self) -> bool:
         """Is the atom affine?
         """
         return self.is_atom_concave() and self.is_atom_convex()
 
+    def is_atom_smooth(self) -> bool:
+        """Is the atom smooth?"""
+        return False
+
     def is_atom_log_log_convex(self) -> bool:
         """Is the atom log-log convex?
         """
@@ -258,6 +262,40 @@ def is_concave(self) -> bool:
             return True
         else:
             return False
+
+    @perf.compute_once
+    def is_linearizable_convex(self) -> bool:
+        """Is the expression convex after linearizing all smooth subexpressions?
+        """
+        # Applies DNLP composition rule.
+        if self.is_constant():
+            return True
+        elif self.is_atom_smooth() or self.is_atom_convex():
+            for idx, arg in enumerate(self.args):
+                if not (arg.is_smooth() or
+                        (arg.is_linearizable_convex() and self.is_incr(idx)) or
+                        (arg.is_linearizable_concave() and self.is_decr(idx))):
+                    return False
+            return True
+        else:
+            return False
+
+    @perf.compute_once
+    def is_linearizable_concave(self) -> bool:
+        """Is the expression concave after linearizing all smooth subexpressions?
+        """
+        # Applies DNLP composition rule.
+        if self.is_constant():
+            return True
+        elif self.is_atom_smooth() or self.is_atom_concave():
+            for idx, arg in enumerate(self.args):
+                if not (arg.is_smooth() or
+                        (arg.is_linearizable_concave() and self.is_incr(idx)) or
+                        (arg.is_linearizable_convex() and self.is_decr(idx))):
+                    return False
+            return True
+        else:
+            return False
 
     def is_dpp(self, context='dcp') -> bool:
         """The expression is a disciplined parameterized expression.
@@ -511,7 +549,7 @@ def _domain(self) -> List['Constraint']:
         """
         # Default is no constraints.
         return []
-
+    
     @staticmethod
     def numpy_numeric(numeric_func):
         """Wraps an atom's numeric function that requires numpy ndarrays as input.

cvxpy/atoms/elementwise/entr.py

@@ -57,6 +57,10 @@ def is_atom_concave(self) -> bool:
         """Is the atom concave?
         """
         return True
+
+    def is_atom_smooth(self) -> bool:
+        """Is the atom smooth?"""
+        return True
 
     def is_incr(self, idx) -> bool:
         """Is the composition non-decreasing in argument idx?
@@ -93,3 +97,6 @@ def _domain(self) -> List[Constraint]:
         """Returns constraints describing the domain of the node.
         """
         return [self.args[0] >= 0]
+
+    def point_in_domain(self):
+        return np.ones(self.shape)

cvxpy/atoms/elementwise/exp.py

@@ -54,6 +54,10 @@ def is_atom_concave(self) -> bool:
         """Is the atom concave?
         """
         return False
+
+    def is_atom_smooth(self) -> bool:
+        """Is the atom smooth?"""
+        return True
 
     def is_atom_log_log_convex(self) -> bool:
         """Is the atom log-log convex?