Summary

lyap and dlyap currently raise ControlArgument("method='scipy' not valid for ...") for three problems, so solving them requires the optional
slycot package:

• the generalized continuous Lyapunov equation A X Eᵀ + E X Aᵀ + Q = 0,
• the generalized discrete Lyapunov equation A X Aᵀ − E X Eᵀ + Q = 0,
• the discrete-time Sylvester equation A X Qᵀ − X + C = 0.

This PR implements pure scipy/numpy fallbacks for all three, so they work
when slycot is not installed. When slycot is installed it remains the
default (method='slycot'), and the new tests cross-validate the scipy
results against it.

What it adds

Generalized Lyapunov (continuous and discrete). The equation is reduced
to a standard Lyapunov equation by a congruence transform with inv(E)
(à = E⁻¹A, Q̃ = E⁻¹Q E⁻ᵀ, leaving X unchanged), then solved with
scipy.linalg.solve_{continuous,discrete}_lyapunov. This requires E to be
nonsingular. SLICOT SG03AD (method='slycot'), based on the generalized
Schur method of Penzl, additionally handles singular E; that case raises a
clear ControlArgument pointing the user to method='slycot'.

Discrete-time Sylvester. Solved by the Bartels–Stewart method: complex
Schur factorizations of A and Qᵀ, then a column-by-column back
substitution. This matches the O(n³ + m³) complexity of the
Hessenberg–Schur algorithm in SLICOT SB04QD (method='slycot').
Singularity (a pair of eigenvalues with λᵢ·λⱼ ≈ 1) is detected and raises
a clear ControlArgument.

Ill-conditioned E. Because the generalized-Lyapunov fallback inverts
E, accuracy degrades when E is poorly conditioned. A UserWarning is
now issued when cond(E) > 1/√eps, recommending method='slycot'. (This
also surfaces a case in the continuous path that was previously silently
inaccurate.)

Fixes included

• Import-alias bug in the optional-slycot guards: the except ImportError branches bound the sentinels under the wrong names
  (sb0qmd for sb04qd, sb04ad for sg03ad), so on a slycot-less
  install the intended None fallbacks were never set under the names the
  code references.
• Stale dlyap docstring note (implied a Kronecker-product method);
  corrected to Bartels–Stewart, with Golub–Nash–Van Loan and
  Bartels–Stewart references added to the Notes/References.

Testing

• scipy↔slycot cross-validation added to test_lyap_g, test_dlyap_g, and
  test_dlyap_sylvester (scipy result matches slycot to ~1e-9…1e-16).
• New test_lyap_g_ill_conditioned_E_scipy (parametrized over lyap/dlyap)
  asserts the ill-conditioned-E UserWarning.
• New test_dlyap_sylvester_singular_scipy asserts the singular-pencil
  ControlArgument.
• Green both with and without slycot: 19 passed, 12 skipped (no slycot) /
  31 passed (slycot).

Only control/mateqn.py and control/tests/mateqn_test.py are touched.

Scope

This is an intentionally small, self-contained slice. It is the first of a
short series factoring a larger "scipy fallbacks for slycot-gated routines"
effort into independently reviewable PRs; the matrix-equation solvers go
first because they are foundational and easy to validate against SLICOT.
gram is the planned next slice.

Alternatives considered

The same inv(E) reduction is used by pyMOR's dense scipy Lyapunov
backend. Factoring the pencil (A, E) with scipy.linalg.qz instead of
inverting E (as harold does) is more robust for ill-conditioned E
and is a natural follow-up. Truly singular E (descriptor systems with
infinite generalized eigenvalues) is out of scope here and continues to
require method='slycot'; a pure-scipy path for that case would need
spectral-projector deflation of the infinite spectrum.