In mathematics, the method of steepest descent or stationary-phase method or saddle-point method is an extension of Laplace's method for approximating an integral, where one deforms a contour integral in the complex plane to pass near a stationary point (saddle point), in roughly the direction of steepest descent or stationary phase. The saddle-point approximation is used with integrals in the complex plane, whereas Laplace’s method is used with real integrals.
The integral to be estimated is often of the form
where C is a contour, and λ is large. One version of the method of steepest descent deforms the contour of integration C into a new path integration C′ so that the following conditions hold:
C′ passes through one or more zeros of the derivative g′(z),
A non-degenerate saddle point, z0 ∈ Cn, of a holomorphic function S(z) is a critical point of the function (i.e., ∇S(z0) = 0) where the function's Hessian matrix has a non-vanishing determinant (i.e., ).
The following is the main tool for constructing the asymptotics of integrals in the case of a non-degenerate saddle point:
The Morse lemma for real-valued functions generalizes as follows for holomorphic functions: near a non-degenerate saddle point z0 of a holomorphic function S(z), there exist coordinates in terms of which S(z) − S(z0) is exactly quadratic. To make this precise, let S be a holomorphic function with domain W ⊂ Cn, and let z0 in W be a non-degenerate saddle point of S, that is, ∇S(z0) = 0 and . Then there exist neighborhoods U ⊂ W of z0 and V ⊂ Cn of w = 0, and a bijective holomorphic function φ : V → U with φ(0) = z0 such that
Without loss of generality, we translate the origin to z0, such that z0 = 0 and S(0) = 0. Using the Auxiliary Statement, we have
Since the origin is a saddle point,
we can also apply the Auxiliary Statement to the functions gi(z) and obtain
Recall that an arbitrary matrix A can be represented as a sum of symmetric A(s) and anti-symmetric A(a) matrices,
The contraction of any symmetric matrix B with an arbitrary matrix A is
i.e., the anti-symmetric component of A does not contribute because
Thus, hij(z) in equation (1) can be assumed to be symmetric with respect to the interchange of the indices i and j. Note that
hence, det(hij(0)) ≠ 0 because the origin is a non-degenerate saddle point.
Let us show by induction that there are local coordinates u = (u1, ... un), z = ψ(u), 0 = ψ(0), such that
First, assume that there exist local coordinates y = (y1, ... yn), z = φ(y), 0 = φ(0), such that
where Hij is symmetric due to equation (2). By a linear change of the variables (yr, ... yn), we can assure that Hrr(0) ≠ 0. From the chain rule, we have
The matrix (Hij(0)) can be recast in the Jordan normal form: (Hij(0)) = LJL−1, were L gives the desired non-singular linear transformation and the diagonal of J contains non-zero eigenvalues of (Hij(0)). If Hij(0) ≠ 0 then, due to continuity of Hij(y), it must be also non-vanishing in some neighborhood of the origin. Having introduced , we write
Motivated by the last expression, we introduce new coordinates z = η(x), 0 = η(0),
The change of the variables y ↔ x is locally invertible since the corresponding Jacobian is non-zero,
Comparing equations (4) and (5), we conclude that equation (3) is verified. Denoting the eigenvalues of by μj, equation (3) can be rewritten as
From equation (6), it follows that . The Jordan normal form of reads , where Jz is an upper diagonal matrix containing the eigenvalues and det P ≠ 0; hence, . We obtain from equation (7)
If , then interchanging two variables assures that .
The asymptotic expansion in the case of a single non-degenerate saddle point
has a single maximum: for exactly one point x0 ∈ Ix;
x0 is a non-degenerate saddle point (i.e., ∇S(x0) = 0 and ).
Then, the following asymptotic holds
where μj are eigenvalues of the Hessian and are defined with arguments
This statement is a special case of more general results presented in Fedoryuk (1987).
Derivation of equation (8)
An illustration to the derivation of equation (8)
First, we deform the contour Ix into a new contour passing through the saddle point x0 and sharing the boundary with Ix. This deformation does not change the value of the integral I(λ). We employ the Complex Morse Lemma to change the variables of integration. According to the lemma, the function φ(w) maps a neighborhood x0 ∈ U ⊂ Ωx onto a neighborhood Ωw containing the origin. The integral I(λ) can be split into two: I(λ) = I0(λ) + I1(λ), where I0(λ) is the integral over , while I1(λ) is over (i.e., the remaining part of the contour I′x). Since the latter region does not contain the saddle point x0, the value of I1(λ) is exponentially smaller than I0(λ) as λ → ∞; thus, I1(λ) is ignored. Introducing the contour Iw such that , we have
Recalling that x0 = φ(0) as well as , we expand the pre-exponential function into a Taylor series and keep just the leading zero-order term
Here, we have substituted the integration region Iw by Rn because both contain the origin, which is a saddle point, hence they are equal up to an exponentially small term. The integrals in the r.h.s. of equation (11) can be expressed as
From this representation, we conclude that condition (9) must be satisfied in order for the r.h.s. and l.h.s. of equation (12) to coincide. According to assumption 2, is a negatively defined quadratic form (viz., ) implying the existence of the integral , which is readily calculated
Equation (8) can also be written as
where the branch of
is selected as follows
Consider important special cases:
If S(x) is real valued for real x and x0 in Rn (aka, the multidimensional Laplace method), then
If S(x) is purely imaginary for real x (i.e., for all x in Rn) and x0 in Rn (aka, the multidimensional stationary phase method), then
The case of multiple non-degenerate saddle points
If the function S(x) has multiple isolated non-degenerate saddle points, i.e.,
is an open cover of Ωx, then the calculation of the integral asymptotic is reduced to the case of a single saddle point by employing the partition of unity. The partition of unity allows us to construct a set of continuous functions ρk(x) : Ωx → [0, 1], 1 ≤ k ≤ K, such that
Therefore as λ → ∞ we have:
where equation (13) was utilized at the last stage, and the pre-exponential function f (x) at least must be continuous.
When ∇S(z0) = 0 and , the point z0 ∈ Cn is called a degenerate saddle point of a function S(z).
Calculating the asymptotic of
when λ → ∞, f (x) is continuous, and S(z) has a degenerate saddle point, is a very rich problem, whose solution heavily relies on the catastrophe theory. Here, the catastrophe theory replaces the Morse lemma, valid only in the non-degenerate case, to transform the function S(z) into one of the multitude of canonical representations. For further details see, e.g., Poston & Stewart (1978) and Fedoryuk (1987).
Integrals with degenerate saddle points naturally appear in many applications including optical caustics and the multidimensional WKB approximation in quantum mechanics.
The other cases such as, e.g., f (x) and/or S(x) are discontinuous or when an extremum of S(x) lies at the integration region's boundary, require special care (see, e.g., Fedoryuk (1987) and Wong (1989)).
An extension of the steepest descent method is the so-called nonlinear stationary phase/steepest descent method. Here, instead of integrals, one needs to evaluate asymptotically solutions of Riemann–Hilbert factorization problems.
Given a contour C in the complex sphere, a function f defined on that contour and a special point, say infinity, one seeks a function M holomorphic away from the contour C, with prescribed jump across C, and with a given normalization at infinity. If f and hence M are matrices rather than scalars this is a problem that in general does not admit an explicit solution.
An asymptotic evaluation is then possible along the lines of the linear stationary phase/steepest descent method. The idea is to reduce asymptotically the solution of the given Riemann–Hilbert problem to that of a simpler, explicitly solvable, Riemann–Hilbert problem. Cauchy's theorem is used to justify deformations of the jump contour.
The nonlinear stationary phase was introduced by Deift and Zhou in 1993, based on earlier work of the Russian mathematician Alexander Its. A (properly speaking) nonlinear steepest descent method was introduced by Kamvissis, K. McLaughlin and P. Miller in 2003, based on previous work of Lax, Levermore, Deift, Venakides and Zhou. As in the linear case, steepest descent contours solve a min-max problem. In the nonlinear case they turn out to be "S-curves" (defined in a different context back in the 80s by Stahl, Gonchar and Rakhmanov).
^Rigorously speaking, this case cannot be inferred from equation (8) because the second assumption, utilized in the derivation, is violated. To include the discussed case of a purely imaginary phase function, condition (9) should be replaced by
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