def cond(x, p=None):
_assertNoEmpty2d(x)
if p in (None, 2):
s = la.svd(x, compute_uv=False)
return s[..., 0] / s[..., -1]
elif p == -2:
s = la.svd(x, compute_uv=False)
r = s[..., -1] / s[..., 0]
else:
_assertRankAtLeast2(x)
_assertNdSquareness(x)
invx = la.inv(x)
r = la.norm(x, ord=p, axis=(-2, -1)) * la.norm(
invx, ord=p, axis=(-2, -1))
# Convert nans to infs unless the original array had nan entries
orig_nan_check = jnp.full_like(r, ~jnp.isnan(r).any())
nan_mask = jnp.logical_and(jnp.isnan(r), ~jnp.isnan(x).any(axis=(-2, -1)))
r = jnp.where(orig_nan_check, jnp.where(nan_mask, jnp.inf, r), r)
return r
def cond(args):
i, lower, _, upper = args
return jnp.logical_and(jnp.less(i, max_it),
jnp.less(abs_tol, jnp.amax(upper - lower)))
def cond_f(args): _, _, j, error = args still_counting = j < maxiter unconverged = error > thresh return jnp.logical_and(still_counting, unconverged)[0]
