Chow and Palmer  provide a proof of the shadowing lemma in 1 dimension when f is a (non-uniformly) expanding map. (Recall that if f is expansive in forward time, then it is contractive in reverse time. Thus we want f to be as expansive as possible when iterated forward, i.e., we want to be as small as possible, to facilitate shadowing.) Let be a pseudo-orbit of a continuous map . They define the ``expansiveness'' as
Each measures how contractive the map is when applied backwards at step i, so that smaller is ``better''. So, each term in the sum measures how contractive the map is when applied backwards from step m down to n. Thus, the larger the sum of absolute values is, the less contractive the map is, when applied backwards from any point beyond n, down to n. Finally, the supremum measures how non-contractive the map can be anywhere. The larger is, the less contractive the inverse map is, and the more difficult it is to shadow the orbit.
Then they define a term I would call the ``error diminishing factor'':
where is the 1-step error of step m+1. The description is similar to that of . For example, the n=0 term measures by how much all the 1-step errors are contracted when they are propagated backwards to step 0.
THEOREM: Let be a function with
Let be a pseudo-orbit of f such that
Then there is an exact orbit with
OUTLINE OF PROOF: We will not discuss the lower bound except to note the interesting fact that it says that the shadow can not be arbitrarily close to the noisy orbit. It is the upper bound that concerns us.
Let the 1-step error at each step be and let be the offset from the noisy orbit to the true orbit we will find, . The are analogous to the correction terms of the GHYS/QT refinement algorithm defined in equation (7). Then we find that the need to satisfy
measures the quality of the 1st order Taylor approximation to , so for small z. It comprises the terms we ignored in equation (7) that go to zero as approaches a true orbit.
Denote by S the set of sequences with for all n. The following mapping T is analogous to 1 refinement of the GHYS/QT algorithm, with the boundary condition . However, it includes, via , the second-and-higher order terms ignored by GHYS/QT, so that if one could actually construct T, it would find the exact shadow in a single ``refinement'' iteration:
Chow and Palmer go on to show that T maps S into itself by showing that and hence that is bounded by . Finally, since T maps S into itself, they invoke Brouwer's Fixed Point Theorem to prove that there is a fixed point of T, proving that a shadow exists within
Since computing and in full would be very expensive ( time), they instead compute
where p is a positive integer chosen as follows. For , we define
Then we choose the smallest p so that . What says in words is, ``ensure that any sequence of p iterations of , taken as a unit, perform a contraction''. Said another way, if , then there does not exist an expansive sequence of backwards iterations of f lasting longer than p. Thus, all backwards sequences of iterations of f longer than p will be contracting, so that, as a whole, the trajectory is ``shadowable''. The larger p is, however, the less shadowable the trajectory becomes until, if , there is too little hyperbolicity. Using a similar argument they define a , using the same p as above.
Finally, Chow and Palmer apply this algorithm to the quadratic map
They show that with a=3.8 and , they can choose p=30, and can shadow the orbit for N=426,000 iterates, with a shadowing distance of . They also perform a detailed analysis of roundoff errors to ensure that their bound is completely rigorous, but we do not detail their analysis here.
Hadeler  relates Chow & Palmer's one dimensional Shadowing Lemma to the older and more well understood work related to Newton's method and Kantorovich's theorem. Kantorovich's theorem is a statement relating G, G', and an initial guess to the convergence of Newton's method. Hadeler shows how to translate between Chow & Palmer's Shadowing Lemma and Kantorovich's theorem, showing the relation between the former's and quantities to quantities needed in order for Kantorovich's theorem to hold. This means that we are closer to being able to apply the vast amount of literature related to Newton's method to shadowing.
Chow and Palmer extended their analysis to maps in higher dimensions . Although the details are different for the multidimensional case, the basic idea is the same as the one dimensional case. Letting be a true orbit offset by from the noisy orbit , one defines an operator L that computes 1-step errors from offsets:
where Z is the set of possible offsets and E is the set of possible 1-step errors. Note that if the space is D dimensional and there are N points in the orbit, then , but , so L is many-to-one. (This is not surprising: there are many different noisy orbits that can possess the same set of 1-step errors.) Finally, our task is to solve . However, since L is many-to-one, is not unique and our task is to choose the with smallest norm, so that is minimized. Chow and Palmer show that if the map has strong hyperbolicity, then the with smallest norm is the one that we would expect if we understand the GHYS/QT algorithm: after decomposing space into the stable and unstable subspaces, has zero component in the stable subspace, while has zero component in the unstable subspace. They demonstrate their method on the Hénon map, and show that for a noisy orbit in double precision with 1-step errors of , they can use p=40 and shadow N=333,000 iterates with an . This means that the shadowing distance is about times the size of the 1-step errors, giving a shadow distance of about .
This ``magnification factor'', the ratio between shadow distance and local error, is termed the ``brittleness'' of an orbit by Dawson et al. . They show that if the number of positive and negative Lyapunov exponents changes, or if a particular one fluctuates about zero, then this magnification factor can blow up. If the brittleness is of order the inverse of the machine epsilon, then all accuracy is lost as the shadowing error is comparable to the size of the variables themselves. The reason that a fluctuating exponent is bad is related in the following diagram, where is the local error, and the vertical direction is initially contracting, but then becomes expanding (Figure 3).
Figure: FLUCTUATING LYAPUNOV EXPONENT IN THE "VERTICAL" DIRECTION.
As we see, (a) an ensemble of trajectories that starts off in an -ball is first compressed into a sheet. (b) If the local error steps outside this sheet, and then the direction becomes an expanding direction, then (c) the numerical trajectory diverges away from all true trajectories that started in the original -ball, (d) possibly entering regions of phase space with qualitatively different behaviour than the true trajectories.
However, Dawson et al. make the strong claim that they believe this kind of fluctuation of Lyapunov exponents is ``common'' in high dimensional systems, with the only justification apparently being that there are so many dimensions, there must be a fluctuating exponent somewhere. It is unclear if this argument applies to all systems. For example, Hamiltonian systems always have equal numbers of positive and negative exponents.