wip: N-D LinearOperator and cg

pixi.toml

@@ -26,7 +26,7 @@ solve-group = "default"
 
 [environments.ipython]
 # tasks: ipython
-features = ["run-deps", "test-deps", "ipython"]
+features = ["run-deps", "test-deps", "ipython-dep", "ipython-task"]
 solve-group = "default"
 
 [environments.build-debug]
@@ -77,8 +77,8 @@ features = ["run-deps", "test-deps", "mkl", "torch-cpu", "torch-cpu-tasks"]
 solve-group = "array-api-cpu"
 
 [environments.array-api-cpu]
-# tasks: test-cpu
-features = ["run-deps", "test-deps", "test-cpu", "mkl", "array_api_strict", "dask", "jax-cpu", "marray", "torch-cpu"]
+# tasks: test-cpu, ipython-cpu
+features = ["run-deps", "test-deps", "test-cpu", "mkl", "array_api_strict", "dask", "jax-cpu", "marray", "torch-cpu", "ipython-dep", "ipython-cpu-task"]
 solve-group = "array-api-cpu"
 
 [environments.build-cuda]
@@ -221,14 +221,20 @@ description = "Build the documentation"
 
 ### IPython ###
 
-[feature.ipython.dependencies]
+[feature.ipython-dep.dependencies]
 ipython = "*"
 
-[feature.ipython.tasks.ipython]
+[feature.ipython-task.tasks.ipython]
 cmd = "spin ipython --no-build"
 depends-on = "build"
 description = "Launch IPython"
 
+[feature.ipython-cpu-task.tasks.ipython-cpu]
+cmd = "spin ipython --build-dir=build-cpu --no-build"
+depends-on = "build-cpu"
+env.SCIPY_ARRAY_API = "1"
+description = "Launch IPython"
+
 
 ### Debugging ###
 

scipy/_lib/_array_api.py

@@ -1028,3 +1028,7 @@ def xp_device_type(a: Array) -> Literal["cpu", "cuda", None]:
         return xp_device_type(a._meta)
     # array-api-strict is a stand-in for unknown libraries; don't special-case it
     return None
+
+
+def xp_isscalar(x):
+    return np.isscalar(x) or (is_array_api_obj(x) and x.ndim == 0)

scipy/sparse/_sputils.py

@@ -369,14 +369,14 @@ def isintlike(x) -> bool:
     return True
 
 
-def isshape(x, nonneg=False, *, allow_nd=(2,)) -> bool:
+def isshape(x, nonneg=False, *, allow_nd=(2,), check_nd=True) -> bool:
     """Is x a valid tuple of dimensions?
 
     If nonneg, also checks that the dimensions are non-negative.
     Shapes of length in the tuple allow_nd are allowed.
     """
     ndim = len(x)
-    if ndim not in allow_nd:
+    if check_nd and ndim not in allow_nd:
         return False
 
     for d in x:

scipy/sparse/linalg/_eigen/_svds.py

@@ -34,6 +34,8 @@ def _iv(A, k, ncv, tol, which, v0, maxiter,
     if math.prod(A.shape) == 0:
         message = "`A` must not be empty."
         raise ValueError(message)
+    if len(A.shape) != 2:
+        raise ValueError("Only 2-D input is supported for `A` (a single matrix)")
 
     # input validation/standardization for `k`
     kmax = min(A.shape) if solver == 'propack' else min(A.shape) - 1

scipy/sparse/linalg/_eigen/tests/test_svds.py

@@ -133,7 +133,7 @@ class SVDSCommonTests:
     _A_empty_msg = "`A` must not be empty."
     _A_dtype_msg = "`A` must be of numeric data type"
     _A_type_msg = "type not understood"
-    _A_ndim_msg = "array must have ndim <= 2"
+    _A_ndim_msg = "Only 2-D input"
     _A_validation_inputs = [
         (np.asarray([[]]), ValueError, _A_empty_msg),
         (np.array([['a', 'b'], ['c', 'd']], dtype='object'), ValueError, _A_dtype_msg),