Add packages cvxpy, clarabel (#4587)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

docs/project/changelog.md

@@ -71,7 +71,8 @@ myst:
 - New Packages: `cysignals`, `ppl`, `pplpy` {pr}`4407`, `flint`, `python-flint` {pr}`4410`,
   `memory_allocator` {pr}`4393`, `primesieve`, `primecount`, `primecountpy` {pr}`4477`,
   `pyxirr` {pr}`4513`, `ipython`, `asttokens`, `executing`, `prompt_toolkit`,
-  `pure_eval`, `stack_data`, `traitlets`, `wcwidth` {pr}`4452`, `altair` {pr}`4580`
+  `pure_eval`, `stack_data`, `traitlets`, `wcwidth` {pr}`4452`, `altair` {pr}`4580`,
+  `cvxpy` {pr}`4587`, `clarabel` {pr}`4587`
 
 - Upgraded `scikit-learn` to 1.4.1.post1 {pr}`4409` and {pr}`4534`
 

packages/clarabel/meta.yaml

@@ -0,0 +1,23 @@
+package:
+  name: clarabel
+  version: 0.7.0
+  top-level:
+    - clarabel
+source:
+  url: https://files.pythonhosted.org/packages/26/06/c95621d326ea8533ed7ece68eb0ef66e69bf49acf72647b53f685371b937/clarabel-0.7.0.tar.gz
+  sha256: b9da56f522806a847f3ece08f5f21fab7506eec3148b22446190de5206dd1e6f
+about:
+  home: https://github.com/oxfordcontrol/Clarabel.rs
+  PyPI: https://pypi.org/project/clarabel
+  summary: Clarabel Conic Interior Point Solver for Rust / Python
+  license: Apache-2.0
+requirements:
+  run:
+    # Dependencies that are needed to run the package
+    - numpy
+    - scipy
+  executable:
+    - rustup
+extra:
+  recipe-maintainers:
+    - phschiele

packages/clarabel/test_clarabel.py

@@ -0,0 +1,37 @@
+import pytest
+from pytest_pyodide import run_in_pyodide
+
+
+@pytest.mark.xfail_browsers(firefox="slow")
+@run_in_pyodide(packages=["clarabel", "scipy", "numpy"])
+def test_clarabel(selenium):
+    import clarabel
+    import numpy as np
+    from scipy import sparse
+
+    # Define problem data
+    P = sparse.csc_matrix([[0.0, 0.0], [0, 0]])
+    P = sparse.triu(P).tocsc()
+
+    q = np.array([-1.0, -4.0])
+
+    A = sparse.csc_matrix(
+        [
+            [1.0, -2.0],  # <-- LHS of equality constraint (lower bound)
+            [1.0, 0.0],  # <-- LHS of inequality constraint (upper bound)
+            [0.0, 1.0],  # <-- LHS of inequality constraint (upper bound)
+            [-1.0, 0.0],  # <-- LHS of inequality constraint (lower bound)
+            [0.0, -1.0],
+        ]
+    )  # <-- LHS of inequality constraint (lower bound)
+
+    b = np.array([0.0, 1.0, 1.0, 1.0, 1.0])
+
+    cones = [clarabel.ZeroConeT(1), clarabel.NonnegativeConeT(4)]
+
+    settings = clarabel.DefaultSettings()
+
+    solver = clarabel.DefaultSolver(P, q, A, b, cones, settings)
+
+    solution = solver.solve()
+    assert solution.status == clarabel.SolverStatus.Solved

packages/cvxpy-base/meta.yaml

@@ -0,0 +1,25 @@
+package:
+  name: cvxpy-base
+  version: 1.5.0
+  top-level:
+    - cvxpy
+source:
+  # TODO: replace with cvxpy-base from PyPI after 1.5 release
+  url: https://github.com/cvxpy/cvxpy/archive/23f1eaa.zip
+  sha256: 4b2a0d982a810d0fdb9f189ab5faa4dbef39679921335e54d737e1b57c555673
+requirements:
+  run:
+    # Dependencies that are needed to run the package
+    - numpy
+    - scipy
+    - clarabel
+about:
+  home: https://github.com/cvxpy/cvxpy
+  PyPI: https://pypi.org/project/cvxpy-base
+  summary:
+    A domain-specific language for modeling convex optimization problems in
+    Python.
+  license: Apache License, Version 2.0
+extra:
+  recipe-maintainers:
+    - phschiele

packages/cvxpy-base/test_cvxpy.py

@@ -0,0 +1,49 @@
+import pytest
+from pytest_pyodide import run_in_pyodide
+
+
+@pytest.mark.xfail_browsers(firefox="slow")
+@run_in_pyodide(packages=["cvxpy-base"])
+def test_cvxpy_clarabel(selenium):
+    import cvxpy as cp
+
+    P = [[3.0, 1.0, -1.0], [1.0, 4.0, 2.0], [-1.0, 2.0, 5.0]]
+
+    q = [1.0, 2.0, -3.0]
+
+    # Create optimization variables
+    x = cp.Variable(3)
+
+    constraints = [x[0] + x[1] - x[2] == 1, x[1] <= 2, x[2] <= 2, cp.SOC(x[0], x[1:])]
+
+    # Form objective.
+    obj = cp.Minimize(0.5 * cp.quad_form(x, P) + q @ x)
+
+    # Form and solve problem.
+    prob = cp.Problem(obj, constraints)
+    prob.solve(solver=cp.CLARABEL)
+    assert prob.status == cp.OPTIMAL
+
+
+@run_in_pyodide(packages=["cvxpy-base"])
+def test_cvxpy_scipy(selenium):
+    import cvxpy as cp
+
+    q = [1.0, 2.0, -3.0]
+
+    # Create optimization variables
+    x = cp.Variable(3)
+
+    constraints = [
+        x[0] + x[1] - x[2] == 1,
+        x[1] >= 2,
+        x[2] <= 2,
+    ]
+
+    # Form objective.
+    obj = cp.Minimize(q @ x)
+
+    # Form and solve problem.
+    prob = cp.Problem(obj, constraints)
+    prob.solve(solver=cp.SCIPY)
+    assert prob.status == cp.OPTIMAL