Merge remote-tracking branch 'upstream/master'

# Conflicts:
#	README.md
#	cvxpy/atoms/__init__.py
#	cvxpy/problems/problem.py
#	cvxpy/reductions/solvers/defines.py
#	cvxpy/reductions/solvers/solving_chain.py
#	cvxpy/settings.py

Author: Transurgeon

.github/workflows/build.yml

@@ -137,8 +137,8 @@ jobs:
           sed -i.bak -e 's/name = "cvxpy"/name = "cvxpy-base"/g' pyproject.toml
           sed -i.bak '/clarabel >= /d' pyproject.toml
           sed -i.bak '/osqp >= /d' pyproject.toml
-          sed -i.bak '/ecos >= /d' pyproject.toml
           sed -i.bak '/scs >= /d' pyproject.toml
+          sed -i.bak '/highspy >= /d' pyproject.toml
           sed -i.bak -e 's/name="cvxpy",/name="cvxpy-base",/g' setup.py
           rm -rf pyproject.toml.bak setup.py.bak
 

.github/workflows/test_optional_solvers.yml

@@ -37,6 +37,9 @@ jobs:
           version: 'lts'
       - name: Install COSMO
         run: julia -e 'using Pkg; Pkg.add("COSMO")'
+      - name: Install SuiteSparse (macOS)
+        if: runner.os == 'macOS'
+        run: brew install suite-sparse
       - name: Install cvxpy dependencies and all optional solvers
         run: uv sync --all-extras --dev
       - name: Print installed solvers

cvxpy/__init__.py

@@ -26,6 +26,7 @@
     NonNeg as NonNeg,
     Zero as Zero,
     PowCone3D as PowCone3D,
+    PowCone3DApprox as PowCone3DApprox,
     PowConeND as PowConeND,
     ExpCone as ExpCone,
     OpRelEntrConeQuad as OpRelEntrConeQuad,
@@ -68,6 +69,7 @@
     CBC as CBC,
     CLARABEL as CLARABEL,
     CUCLARABEL as CUCLARABEL,
+    PDCS as PDCS,
     COPT as COPT,
     COSMO as COSMO,
     CPLEX as CPLEX,

cvxpy/atoms/__init__.py

@@ -14,7 +14,8 @@
 limitations under the License.
 """
 
-from cvxpy.atoms.affine.binary_operators import (matmul, multiply,
+from cvxpy.atoms.affine.add_expr import AddExpression
+from cvxpy.atoms.affine.binary_operators import (matmul, MulExpression, multiply,
                                                  vdot, scalar_product, outer,)
 from cvxpy.atoms.affine.bmat import bmat
 from cvxpy.atoms.affine.broadcast_to import broadcast_to
@@ -36,7 +37,7 @@
 from cvxpy.atoms.affine.squeeze import squeeze
 from cvxpy.atoms.affine.concatenate import concatenate
 from cvxpy.atoms.affine.stack import stack
-from cvxpy.atoms.affine.sum import sum
+from cvxpy.atoms.affine.sum import Sum, sum
 from cvxpy.atoms.affine.trace import trace, Trace
 from cvxpy.atoms.affine.transpose import (transpose, permute_dims, swapaxes, moveaxis)
 from cvxpy.atoms.affine.upper_tri import upper_tri, vec_to_upper_tri
@@ -167,3 +168,30 @@
     maximum,
     minimum,
 ]
+
+# DGP atoms whose Dgp2Dcp canonicalization produces ExpCone-requiring DCP atoms.
+GP_EXP_ATOMS = [
+    AddExpression,
+    exp,
+    eye_minus_inv,
+    log,
+    MulExpression,
+    norm1,
+    one_minus_pos,
+    Pnorm,
+    PnormApprox,
+    QuadForm,
+    Sum,
+    Trace,
+    xexp,
+]
+
+# DGP atoms whose Dgp2Dcp canonicalization produces NonNeg constraints.
+GP_NONPOS_ATOMS = [
+    norm_inf,
+    max,
+    min,
+    maximum,
+    minimum,
+    pf_eigenvalue,
+]

cvxpy/constraints/__init__.py

@@ -20,7 +20,7 @@
                                            RelEntrConeQuad,)
 from cvxpy.constraints.finite_set import FiniteSet
 from cvxpy.constraints.nonpos import Inequality, NonNeg, NonPos
-from cvxpy.constraints.power import PowCone3D, PowConeND
+from cvxpy.constraints.power import PowCone3D, PowCone3DApprox, PowConeND
 from cvxpy.constraints.psd import PSD
 from cvxpy.constraints.second_order import SOC
 from cvxpy.constraints.zero import Equality, Zero

cvxpy/constraints/power.py

@@ -157,6 +157,17 @@ def _dual_cone(self, *args):
                              args[2], self.alpha)
 
 
+class PowCone3DApprox(PowCone3D):
+    """PowCone3D with SOC-based rational approximation.
+
+    Identical semantics to PowCone3D, but the solving chain will
+    convert this constraint to second-order cone (SOC) constraints
+    via rational approximation of the exponent, following the same
+    pattern as PowerApprox / PnormApprox for atoms.
+    """
+    pass
+
+
 class PowConeND(Cone):
     """
     Represents a collection of N-dimensional power cone constraints
@@ -237,7 +248,12 @@ def shape(self) -> Tuple[int, int]:
         # This constitutes the shape of the hypograph variable z
         # appended to W in the standard conic form.
         # TODO: support arbitrary z.dim
-        m, n = self.W.shape if self.axis == 0 else (self.W.shape[1], self.W.shape[0])
+        if self.W.ndim == 1:
+            m, n = self.W.shape[0], 1
+        elif self.axis == 0:
+            m, n = self.W.shape
+        else:
+            m, n = self.W.shape[1], self.W.shape[0]
         s = (m + 1, n)
         return s
 
@@ -285,13 +301,14 @@ def is_dqcp(self) -> bool:
         return self.is_dcp()
 
     def save_dual_value(self, value) -> None:
-        dW = value[:, :-1]
-        dz = value[:, -1]
-        if self.axis == 0:
+        # Value has shape (n+1, k) from ConeMatrixStuffing (constraint.shape).
+        # First n rows are W duals, last row is z duals.
+        dW = value[:-1, :]  # Shape (n, k)
+        dz = value[-1, :]   # Shape (k,)
+        if self.axis == 1:
             dW = dW.T
-            dz = dz.T
-        if dW.shape[1] == 1:
-            #NOTE: Targetting problems where duals have the shape
+        if dW.shape[-1] == 1:
+            # NOTE: Targetting problems where duals have the shape
             # (n, 1) --- dropping the extra dimension is crucial for
             # the `_dual_cone` and `dual_residual` methods to work properly
             dW = np.squeeze(dW)

cvxpy/expressions/expression.py

@@ -20,6 +20,7 @@
 from typing import List, Literal, Optional, Self, Tuple
 
 import numpy as np
+import scipy.sparse as sp
 
 import cvxpy as cp
 import cvxpy.settings as s
@@ -542,13 +543,14 @@ def is_nonpos(self) -> bool:
         raise NotImplementedError()
 
     @abc.abstractmethod
-    def get_bounds(self) -> Tuple[np.ndarray, np.ndarray]:
+    def get_bounds(self) -> tuple[np.ndarray | sp.sparray, np.ndarray | sp.sparray]:
         """Returns bounds (lower, upper) of the expression.
 
         Returns
         -------
-        tuple of np.ndarray
+        tuple of (np.ndarray | sp.sparray)
             (lower_bound, upper_bound) arrays with shape matching self.shape.
+            For sparse variables with sparse bounds, may return sparse arrays.
         """
         raise NotImplementedError()