Resolve issue #395

docs/source/notes.rst

@@ -193,15 +193,28 @@ the gradient of ``f`` at 0, :func:`scico.grad` returns ``nan``:
    >>> scico.grad(f)(snp.zeros(2, dtype=snp.float32))  # doctest: +SKIP
    Array([nan, nan], dtype=float32)
 
-This can be fixed by defining the squared :math:`\ell_2` norm directly as
-``g = lambda x: snp.sum(x**2)``. The gradient will work as expected:
+This can be fixed (assuming real-valued arrays only) by defining the
+squared :math:`\ell_2` norm directly as ``g = lambda x: snp.sum(x**2)``.
+The gradient will work as expected:
 
 ::
 
    >>> g = lambda x: snp.sum(x**2)
    >>> scico.grad(g)(snp.zeros(2, dtype=snp.float32))  #doctest: +SKIP
    Array([0., 0.], dtype=float32)
 
+If complex-valued arrays also need to be supported, a minor modification is
+necessary:
+
+::
+
+   >>> g = lambda x: snp.sum(snp.abs(x)**2)
+   >>> scico.grad(g)(snp.zeros(2, dtype=snp.float32))  #doctest: +SKIP
+   Array([0., 0.], dtype=float32)
+   >>> scico.grad(g)(snp.zeros(2, dtype=snp.complex64))  #doctest: +SKIP
+   Array([0.-0.j, 0.-0.j], dtype=complex64)
+
+
 An alternative is to define a `custom derivative rule
 <https://jax.readthedocs.io/en/latest/notebooks/Custom_derivative_rules_for_Python_code.html#enforcing-a-differentiation-convention>`_
 to enforce a particular derivative convention at a point.
@@ -259,13 +272,6 @@ DeviceArrays are Immutable
 
 Unlike standard NumPy arrays, JAX arrays are immutable: once they have
 been created, they cannot be changed. This prohibits in-place updating
-of JAX arrays.
-
-JAX provides special syntax for updating individual array elements
-through the `indexed update operators
+of JAX arrays. JAX provides special syntax for updating individual
+array elements through the `indexed update operators
 <https://jax.readthedocs.io/en/latest/jax.ops.html#syntactic-sugar-for-indexed-update-operators>`_.
-
-In-place operations such as `x += y` must be replaced with the
-out-of-place version `x = x + y`. Note that these operations will be
-optimized if they are placed inside of a `jitted function
-<https://jax.readthedocs.io/en/latest/notebooks/thinking_in_jax.html#to-jit-or-not-to-jit>`_.

scico/optimize/_admmaux.py

@@ -1,5 +1,5 @@
 # -*- coding: utf-8 -*-
-# Copyright (C) 2020-2022 by SCICO Developers
+# Copyright (C) 2020-2023 by SCICO Developers
 # All rights reserved. BSD 3-clause License.
 # This file is part of the SCICO package. Details of the copyright and
 # user license can be found in the 'LICENSE' file distributed with the
@@ -22,7 +22,6 @@
 from scico.linop import CircularConvolve, Identity, LinearOperator
 from scico.loss import SquaredL2Loss
 from scico.numpy import BlockArray
-from scico.numpy.linalg import norm
 from scico.numpy.util import ensure_on_device, is_real_dtype
 from scico.solver import cg as scico_cg
 from scico.solver import minimize
@@ -63,6 +62,8 @@ def internal_init(self, admm: soa.ADMM):
 class GenericSubproblemSolver(SubproblemSolver):
     """Solver for generic problem without special structure.
 
+    Note that this solver is only suitable for small-scale problems.
+
     Attributes:
         admm (:class:`.ADMM`): ADMM solver object to which the solver is
            attached.
@@ -98,9 +99,9 @@ def obj(x):
             for rhoi, Ci, zi, ui in zip(
                 self.admm.rho_list, self.admm.C_list, self.admm.z_list, self.admm.u_list
             ):
-                out = out + 0.5 * rhoi * norm(zi - ui - Ci(x)) ** 2
+                out += 0.5 * rhoi * snp.sum(snp.abs(zi - ui - Ci(x)) ** 2)
             if self.admm.f is not None:
-                out = out + self.admm.f(x)
+                out += self.admm.f(x)
             return out
 
         res = minimize(obj, x0, **self.minimize_kwargs)
@@ -211,7 +212,7 @@ def internal_init(self, admm: soa.ADMM):
         )
         if admm.f is not None:
             # hessian = A.T @ W @ A; W may be identity
-            lhs_op = lhs_op + admm.f.hessian
+            lhs_op += admm.f.hessian
 
         lhs_op.jit()
         self.lhs_op = lhs_op
@@ -240,7 +241,7 @@ def compute_rhs(self) -> Union[JaxArray, BlockArray]:
         for rhoi, Ci, zi, ui in zip(
             self.admm.rho_list, self.admm.C_list, self.admm.z_list, self.admm.u_list
         ):
-            rhs = rhs + rhoi * Ci.adj(zi - ui)
+            rhs += rhoi * Ci.adj(zi - ui)
         return rhs
 
     def solve(self, x0: Union[JaxArray, BlockArray]) -> Union[JaxArray, BlockArray]:

scico/optimize/_primaldual.py

@@ -44,7 +44,7 @@ class PDHG(Optimizer):
     where :math:`f` and :math:`g` are instances of :class:`.Functional`,
     (in most cases :math:`f` will, more specifically be an an instance
     of :class:`.Loss`), and :math:`C` is an instance of
-    :class:`.LinearOperator`.
+    :class:`.Operator` or :class:`.LinearOperator`.
 
     When `C` is a :class:`.LinearOperator`, the algorithm iterations are