(11.24)
11.5
METHOD
OF
STEEPEST
DESCENT
313
be helpful
if
we could find a general approximation for it that is applicable for
all f and g.
The
fact that
lal
is large will be
of
great help. By redefining
f(z),
if
necessary, we can assume that
a =
lale'
arg(a) is real and positive [absorb e'
argte)
into the function
f(z)
if
needbe].
The exponent
of
the integrand
can
be written as
af(z)
=
au(x,
y) +
iav(x,
y).
Since a is large and positive, we expect the exponential to be the largest at the
maximum
of
u(x,
y). Thus, if we deform the contour so that it passes through a
point zo at which
u(x, y) is maximum, the contribution to the integral may come
mostly from the neighborhood
of
zoo
This opens up the possibility
of
expanding
the exponent about zo and keeping the lowest terms in the expansion, which is
what we are after. There is one catch, however. Because
of
the largeness
of
a, the
imaginary part
of
af
in the exponent will oscillate violently as
v(x,
y) changes
even by a small amount. This oscillation can make the contribution
of
the real
part
of
f(zo)
negligibly small and render the whole procedure useless. Thus, we
want to tame the variation
of
exp[iv(x,
y)] by making
v(x,
y) vary as slowly as
possible. A necessary condition is for the derivative
of
v to vanish at
zoo
This and
the fact that the real part is to have a maximum at zo lead to
au . av
dfl
ax +Z ax =
dz
zo
=
o.
However, we do not stop here
but
demandthatthe imaginary part
of
f be constant
along the deformed contour:
Im[f(z)]
=
Im[f(zo)]
or
v(x,
y) =v(xo, Yo).
Equation (11.24) and the Cauchy-Riemann conditions imply that
aujax
=
0=
aujay
atzo.
Thus, it might appearthatzo is a maximum (or minimum)
of
the
surface described by the function
u(x,
y). This is not true: For the surface to have
a maximum (minimum), both second derivatives, a
2ujax
2
and a
2uja
y2, must be
negative (positive). But thatis impossiblebecause
u(x, y) is
harmonic-the
sum
of
these two derivatives is zero. Recall that a pointat whichthe derivatives vanish
but
that is neither a maximum
nor
a minimumis called a saddlepoint. That is why the
procedure described below is sometimes calledthe
saddle
point
approximation.
We are interested in values
of
z close to zo. So let us expand
f(z)
in a Taylor
series about zo, use Equation (11.24), and keep terms only up to the second, to
obtain
f(z)
=
f(zo)
+
!(z
- ZO)2I"(zo).
Let
us assume that
t"
(zo)
f=
0, and define
z - zo
=
Yje
iB,
and
!f"(zo)
= T2eifh
and substitute in the above expansion to obtain
f(z)
-
f(zo)
=
TfT2ei(2Bz+fh),
(11.25)
(11.26)
(11.27)