Merge branch 'master' into cvxpy-sync-1.9

Author: SteveDiamond

.github/workflows/cvxpygen.yml .github/workflows/extensions.yml.github/workflows/cvxpygen.yml renamed to .github/workflows/extensions.yml

@@ -1,4 +1,4 @@
-name: test_cvxpygen
+name: test_extensions
 
 on:
     pull_request:
@@ -21,14 +21,26 @@ jobs:
         run: |
           sudo apt update
           sudo apt install libeigen3-dev
-      - name: Install cvxpy and CVXPYgen
+      - name: Install CVXPY
         run: |
-          uv venv
+          uv venv --seed
           uv pip install .
+      - name: Install CVXPYlayers
+        run: |
+          git clone https://github.com/cvxpy/cvxpylayers.git
+          cd cvxpylayers
+          uv pip install ".[torch, jax]"
+          uv pip install "mlx[cpu]" "mpax"
+      - name: Install CVXPYgen
+        run: |
           git clone --recurse-submodules https://github.com/cvxgrp/cvxpygen.git
           cd cvxpygen
           uv pip install .[dev]
-          uv pip install cvxpylayers==0.1.9 "torch>=2.3" jax==0.5.3
+      - name: Run CVXPYlayers tests
+        run: |
+          uv run --no-sync pytest cvxpylayers/tests \
+            --ignore=cvxpylayers/tests/test_cuclarabel.py
       - name: Run CVXPYgen tests
+        if: success() || failure()
         run: |
           uv run --no-sync pytest cvxpygen/tests

.github/workflows/scorecards.yml

@@ -51,6 +51,6 @@ jobs:
       # Upload the results to GitHub's code scanning dashboard (optional).
       # Commenting out will disable upload of results to your repo's Code Scanning dashboard
       - name: "Upload to code-scanning"
-        uses: github/codeql-action/upload-sarif@c10b8064de6f491fea524254123dbe5e09572f13 # v4.35.1
+        uses: github/codeql-action/upload-sarif@68bde559dea0fdcac2102bfdf6230c5f70eb485e # v4.35.4
         with:
           sarif_file: results.sarif

.github/workflows/test_backends.yml

@@ -11,24 +11,28 @@ on:
 jobs:
   test_backends:
     runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        backend: [SCIPY, COO]
     steps:
-      - uses: actions/setup-python@v6
+      - uses: actions/checkout@v5
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v7
         with:
           python-version: "3.12"
-      - uses: actions/checkout@v5
+          enable-cache: true
+
       - name: Install cvxpy dependencies
         run: |
-          pip install -e .
-          pip install pytest hypothesis
-      - name: Run tests with SCIPY backend
-        run : |
-          export CVXPY_DEFAULT_CANON_BACKEND="SCIPY"
-          python -c "from cvxpy.cvxcore.python.canonInterface import get_default_canon_backend; print(get_default_canon_backend())"
-          python -c "from cvxpy.cvxcore.python.canonInterface import get_default_canon_backend; assert get_default_canon_backend() == 'SCIPY'"
-          pytest
-      - name: Run tests with COO backend
-        run : |
-          export CVXPY_DEFAULT_CANON_BACKEND="COO"
-          python -c "from cvxpy.cvxcore.python.canonInterface import get_default_canon_backend; print(get_default_canon_backend())"
-          python -c "from cvxpy.cvxcore.python.canonInterface import get_default_canon_backend; assert get_default_canon_backend() == 'COO'"
-          pytest
+          uv venv
+          uv pip install -e ".[testing]"
+
+      - name: Run tests with ${{ matrix.backend }} backend
+        env:
+          CVXPY_DEFAULT_CANON_BACKEND: ${{ matrix.backend }}
+        run: |
+          uv run python -c "from cvxpy.cvxcore.python.canonInterface import get_default_canon_backend; print(get_default_canon_backend())"
+          uv run python -c "from cvxpy.cvxcore.python.canonInterface import get_default_canon_backend; assert get_default_canon_backend() == '${{ matrix.backend }}'"
+          uv run pytest

.github/workflows/test_derivative.yml

@@ -0,0 +1,26 @@
+name: test_derivative
+
+on:
+  pull_request:
+  push:
+    branches:
+      - master
+
+jobs:
+  test_derivative:
+    runs-on: ubuntu-22.04
+    defaults:
+      run:
+        shell: bash -l {0}
+    steps:
+      - uses: actions/checkout@v5
+      - uses: astral-sh/setup-uv@v7
+        with:
+          python-version: "3.12"
+          enable-cache: true
+      - name: Install cvxpy and diffcp
+        run: |
+          uv sync --dev
+          uv pip install pytest diffcp
+      - name: Run test_derivative
+        run: uv run pytest -rs cvxpy/tests/test_derivative.py

cvxpy/__init__.py

@@ -64,6 +64,7 @@
     suppfunc as suppfunc,
 )
 from cvxpy import logic as logic
+from cvxpy import nlp as nlp
 from cvxpy.reductions.solvers.defines import installed_solvers as installed_solvers
 from cvxpy.settings import (
     CBC as CBC,

cvxpy/atoms/__init__.py

@@ -72,9 +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.elementwise.normcdf import normcdf
+# NLP atoms (require nlp=True solver) are accessible via cp.nlp namespace only.
+# e.g. cp.nlp.sin, cp.nlp.cos  — not at the top-level cp namespace.
 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

cvxpy/atoms/quad_form.py

@@ -17,15 +17,14 @@
 
 import numpy as np
 import scipy.sparse as sp
-from scipy import linalg as LA
 
 from cvxpy.atoms.affine.wraps import psd_wrap
 from cvxpy.atoms.atom import Atom
 from cvxpy.expressions.constants.parameter import is_param_affine, is_param_free
 from cvxpy.expressions.expression import Expression
 from cvxpy.interface.matrix_utilities import is_sparse
 from cvxpy.utilities import scopes
-from cvxpy.utilities.linalg import sparse_cholesky
+from cvxpy.utilities.linalg import dense_ldl_decomp, sparse_cholesky
 from cvxpy.utilities.warn import warn
 
 
@@ -264,27 +263,7 @@ def decomp_quad(P, cond=None, rcond=None, lower=True, check_finite: bool = True)
                 return 1.0, np.empty((0, 0)), L[p, :]
         except ValueError:
             P = P.toarray()  # make dense (needs to happen for ldl).
-    lu, d, _perm = LA.ldl(P, lower=lower, check_finite=check_finite)
-
-    # Extract effective diagonal values from D, handling any 2x2 blocks.
-    # For PSD/NSD matrices D is diagonal (no 2x2 blocks). For indefinite
-    # matrices, Bunch-Kaufman pivoting may introduce 2x2 blocks which we
-    # resolve by batching all 2x2 blocks into a single eigh call.
-    sub_diag = np.diag(d, -1)
-    block_starts = np.nonzero(sub_diag)[0]
-    diag_vals = np.real(np.diag(d)).copy()
-    if len(block_starts) > 0:
-        bs = block_starts
-        idx = bs[:, None] + np.arange(2)[None, :]  # (k, 2)
-        blocks = d[idx[:, :, None], idx[:, None, :]]  # (k, 2, 2)
-        eigvals, eigvecs = np.linalg.eigh(blocks)  # (k, 2), (k, 2, 2)
-        diag_vals[bs] = eigvals[:, 0]
-        diag_vals[bs + 1] = eigvals[:, 1]
-        # Apply eigenvector rotations to lu columns
-        lu_i = lu[:, bs].copy()
-        lu_ip1 = lu[:, bs + 1].copy()
-        lu[:, bs] = lu_i * eigvecs[:, 0, 0] + lu_ip1 * eigvecs[:, 1, 0]
-        lu[:, bs + 1] = lu_i * eigvecs[:, 0, 1] + lu_ip1 * eigvecs[:, 1, 1]
+    diag_vals, lu = dense_ldl_decomp(P, lower=lower, check_finite=check_finite)
 
     if rcond is not None:
         cond = rcond

cvxpy/expressions/leaf.py

@@ -167,7 +167,6 @@ def __init__(
                 "Sparsity and diag attributes force zeros, which contradicts "
                 "strict positivity/negativity."
             )
-        self._leaf_of_provenance = None
         self.args = []
         self.bounds = self._ensure_valid_bounds(bounds)
         self.attributes['bounds'] = self.bounds
@@ -709,18 +708,6 @@ def is_dpp(self, context: str = 'dcp') -> bool:
     def atoms(self) -> list[Atom]:
         return []
 
-    def attributes_were_lowered(self) -> bool:
-        """True iff this leaf was generated when lowering a leaf with attributes."""
-        return self._leaf_of_provenance is not None
-
-    def set_leaf_of_provenance(self, leaf: Leaf) -> None:
-        assert leaf.attributes
-        self._leaf_of_provenance = leaf
-
-    def leaf_of_provenance(self) -> Leaf | None:
-        """Returns a leaf with attributes from which this leaf was generated."""
-        return self._leaf_of_provenance
-
     @property
     def _has_dim_reducing_attr(self) -> bool:
         return (self.sparse_idx is not None or self.attributes['diag'] or

cvxpy/expressions/variable.py

@@ -126,14 +126,6 @@ def canonicalize(self) -> tuple[Expression, list[Constraint]]:
         obj = lu.create_var(self.shape, self.id)
         return (obj, [])
 
-    def set_variable_of_provenance(self, variable: Variable) -> None:
-        """Deprecated: use set_leaf_of_provenance instead."""
-        self.set_leaf_of_provenance(variable)
-
-    def variable_of_provenance(self) -> Variable | None:
-        """Deprecated: use leaf_of_provenance instead."""
-        return self.leaf_of_provenance()
-
     def __repr__(self) -> str:
         """String to recreate the variable."""
         attr_str = self._get_attr_str()

cvxpy/nlp/__init__.py

@@ -0,0 +1,19 @@
+"""
+cvxpy.nlp — Namespace for NLP (nonlinear programming) atoms.
+
+These atoms require a solver that supports nlp=True (e.g. IPOPT, UNO).
+
+Example usage:
+    import cvxpy as cp
+    x = cp.Variable()
+    prob = cp.Problem(cp.Minimize(cp.nlp.sin(x)), [x >= 0])
+    prob.solve(nlp=True)
+"""
+
+from cvxpy.atoms.elementwise.trig import sin, cos, tan
+from cvxpy.atoms.elementwise.hyperbolic import sinh, tanh, asinh, atanh
+
+__all__ = [
+    "sin", "cos", "tan",
+    "sinh", "tanh", "asinh", "atanh",
+]