1. The Steepest-Descent Method
ECE 6382
Fall 2016
David R. Jackson
Notes 14
The Steepest-Descent Method
Notes are adapted from ECE 6341

2. Steepest-Descent Method
Complex Integral:
The method was published by Peter Debye in Debye noted in his work that the method was developed in a unpublished note by Bernhard Riemann (1863).
Peter Joseph William Debye (March 24, 1884 – November 2, 1966) was a Dutch physicist and physical chemist, and Nobel laureate in Chemistry.
Georg Friedrich Bernhard Riemann (September 17, 1826 – July 20, 1866) was an influential German mathematician who made lasting contributions to analysis and differential geometry, some of them enabling the later development of general relativity.

3. Steepest-Descent Method (cont.)
Complex Integral:
We want to obtain an approximate evaluation of the integral when the real parameter Ω is very large.
The functions f (z) and g(z) are analytic (except for poles or branch points), so that the path C may be deformed if necessary (possibly adding residue contributions or branch-cut integrals).
Saddle Point (SP):

4. Steepest-Descent Method (cont.)
Path deformation:
If the path does not go through a saddle point, we assume that it can be deformed to do so.
If any singularities are encountered during the path deformation, they must be accounted for (e.g., residue of captured poles). X

5. Steepest-Descent Method (cont.)
Denote
Cauchy Reimann eqs.:
Hence

6. Steepest-Descent Method (cont.)or If
then
Near the saddle point:
Ignore (rotate coordinates
to eliminate)

7. Steepest-Descent Method (cont.)
In the rotated coordinate system:
Assume that the coordinate system is rotated so that
Note:
The angle of rotation necessary to do this will be clear later
(it is the departure angle SDP of the steepest-descent path).

8. Steepest-Descent Method (cont.)
The u (x, y ) function has a “saddle” shape near the SP:

9. Steepest-Descent Method (cont.)
Note: The saddle does not necessarily open along one of the principal axes (only when uxy (x0, y0) = 0).

10. Steepest-Descent Method (cont.)
A descending path is one on which the u function decreases away from the saddle point.
3D view of descending path

11. Steepest-Descent Method (cont.)
Along any descending path C we will have convergence:
Exponentially decreasing function

12. Steepest-Descent Method (cont.)
Behavior on a descending path
Note:
The parameter s is a measure of distance along the path from the saddle point.
(Negative s means before we get to the saddle point, positive s means after we leave the saddle point.)
When  is large most of contribution is from near z0.

13. Steepest-Descent Method (cont.)
Sketches of a descending path:

14. Steepest-Descent Method (cont.)
Along any descending path:
Both the phase and amplitude change along an arbitrary descending path C.
Important Point: If we can find a path along which the phase does not change (v is constant), the integrand will have a purely exponentially decaying behavior (no phase term), making the integral easy to evaluate.

15. Steepest-Descent Method (cont.)
Choose path of constant phase:

16. Steepest-Descent Method (cont.)
Gradient Property (proof on next slide):
Hence C0 is either a “path of steepest descent” (SDP) or a “path of steepest ascent” (SAP).
SDP: u(x,y) decreases as fast as possible along the path away from the saddle point.
SAP: u(x,y) increases as fast as possible along the path away from the saddle point.

17. Steepest-Descent Method (cont.)
Proof
C.R. Equations x y C0
Hence,
Also,
Hence

18. Steepest-Descent Method (cont.)
Because the v function is constant along the SDP, we have: or
Real

19. Steepest-Descent Method (cont.)
Local behavior near SP so y
z0+dz r  z0
Denote x

20. Steepest-Descent Method (cont.)
SAP:
SDP:
Note: The two paths are 90o apart at the saddle point.

21. Steepest-Descent Method (cont.)
SAP
SDP SP x y
u decreases
u increases
90o
Note: v(x,y) is constant along both paths.

22. Steepest-Descent Method (cont.)
The landscape of the function u(z) near z0
Valley
Hill

23. Steepest-Descent Method (cont.)
SAP
SDP

24. Steepest-Descent Method (cont.)
Set
This defines
SDP

25. Steepest-Descent Method (cont.)
We then have or
(leading term of the asymptotic expansion)
Hence

26. Steepest-Descent Method (cont.)
To evaluate the derivative:
(Recall: v is constant along SDP.)
At the SP this gives 0 = 0.
Take one more derivative:

27. Steepest-Descent Method (cont.)At so
Hence, we have

28. Steepest-Descent Method (cont.)
There is an ambiguity in sign for the square root:
To avoid this ambiguity, define

29. Steepest-Descent Method (cont.)
The derivative term is therefore
Hence

30. Steepest-Descent Method (cont.)
To find SDP :
Denote

31. Steepest-Descent Method (cont.)
SDP
SAP
The direction of
integration determines
The sign.
The “user” must determine this.

32. Steepest-Descent Method (cont.)
Summary

33. Example
where
Hence, we identify:

34. Example (cont.) x y

35. Example (cont.)
Identify the SDP and SAP:
SDP and SAP:

36. Example (cont.) SDP and SAP: SAP
Examination of the u function reveals which of the two paths is the SDP.

37. Example (cont.)
SDP
Vertical paths are added so that the path now has limits at infinity.
SDP = C + Cv1 + Cv2
It is now clear which choice is correct for the departure angle:

38. Example (cont.)
(If we ignore the contributions of the vertical paths.)
Hence, so

39. Example (cont.)
Hence

40. Example (cont.)
Examine the path Cv1 (the path Cv2 is similar).
Let

41. Example (cont.) since Since  is becoming very large, we can write:
Hence, we have

42. Example (cont.)
Hence,
Hence, Iv1 is a much smaller term than what we obtain from the method of steepest descents, when  gets large, and we can ignore it.

43. Example The Gamma (generalized factorial) function: Let
Note: There is no saddle point!
Let

44. Example (cont.) or

45. Example (cont.) Recall: Hence or This is Sterling’s formula. Note:
The departure angle is zero, not , since we are integrating from 0 to . The SDP is the real axis. or
This is Sterling’s formula.

46. Complete Asymptotic Expansion
We can obtain the complete asymptotic expansion of the integral with the steepest-descent method
(including as many higher-order terms as we wish).
Define:
Then we have

47. Complete Asymptotic Expansion (cont.)
(Extending the limits to infinity gives an exponentially small error:
This does not affect the asymptotic expansion.)
Assume
Then
Note: The integral is zero for n = odd.
This result is called “Watson’s Lemma”:
We can obtain the complete asymptotic expansion of I by substituting in the complete asymptotic expansion for h and integrating term-by-term.

48. Complete Asymptotic Expansion (cont.)
Performing the integration,
where
We then have

49. Complete Asymptotic Expansion (cont.)
Summary

50. Complete Asymptotic Expansion (cont.)
Example:
We have
SDP: y = 0 (real axis)
SAP: x = 0 (imaginary axis)

51. Complete Asymptotic Expansion (cont.)
Also, we have x y
Hence
We then have

52. Complete Asymptotic Expansion (cont.)
Hence
Recall:

53. Complete Asymptotic Expansion (cont.)
The complete asymptotic expansion is
Hence we have:
so that
where
(Please see the notes on the Gamma function.)

54. Complete Asymptotic Expansion (cont.)
Hence we have: as
Note:
In a similar manner, we could obtain higher-order terms in the asymptotic expansion of the Bessel function or the Gamma function.