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. 2020 Aug 28;4(4):509–523. doi: 10.1007/s41468-020-00059-7

Contractibility of a persistence map preimage

Jacek Cyranka 1,2,, Konstantin Mischaikow 1, Charles Weibel 1
PMCID: PMC7548282  PMID: 33094152

Abstract

This work is motivated by the following question in data-driven study of dynamical systems: given a dynamical system that is observed via time series of persistence diagrams that encode topological features of snapshots of solutions, what conclusions can be drawn about solutions of the original dynamical system? We address this challenge in the context of an N dimensional system of ordinary differential equation defined in RN. To each point in RN (e.g. an initial condition) we associate a persistence diagram. The main result of this paper is that under this association the preimage of every persistence diagram is contractible. As an application we provide conditions under which multiple time series of persistence diagrams can be used to conclude the existence of a fixed point of the differential equation that generates the time series.

Keywords: Topological data analysis, Persistent homology, Dynamical systems, Fixed point theorem

Introduction

Topological data analysis (TDA), especially in the form of persistent homology, is rapidly developing into a widely used tool for the analysis of high dimensional data associated with nonlinear structures (Edelsbrunner and Harer 2010; Zomorodian and Carlsson 2005; Oudot 2015). That topological tools can play a role in this subject should not be unexpected, given the central role of nonlinear functional analysis in the study of geometry, analysis, and differential equations, for example. What is perhaps surprising is that, to the best of our knowledge, there have been no systematic attempts to rigorously analyze the dynamics of differential equations using persistent homology.

Persistent homology is often used as a means of data reduction. A typical example takes the form of a complicated scalar function defined over a fixed domain, where the geometry of the sub-(super)-level sets is encoded via homology. Of particular interest to us are settings in which the scalar function arises as a solution to a partial differential equation (PDE); we are interested in tracking the evolution of the function, but experimental data only provides information on the level of digital images of the process. Furthermore, capturing the dynamics of a PDE often requires a long time series of rather large digital images. Thus, rather than storing the full images, one can hope to work with a time series of persistence diagrams. Our aim is to draw conclusions about the dynamics of the original PDE from the time series of the persistence diagrams. This is an extremely ambitious goal and far beyond our capabilities at the moment. A much simpler question is the following: if there is an attracting region in the space of persistence diagrams, under what conditions can we conclude that there is a fixed point for the PDE?

This paper represents a first step towards answering the simpler question. Theorem 4.3 shows that given an ordinary differential equation (ODE) with a global compact attractor ARN and a neighborhood in the space of persistence diagrams that is mapped into itself under the dynamics, then there exists a fixed point for the ODE. In applications one could consider the ODE as arising from a finite difference approximation of the PDE.

The challenge is that to obtain results one must understand the topology of dataP, the space of data having a fixed persistence diagram P, a topic for which there are only limited results. That the structure of dataP is complicated follows directly from the fact that persistent homology can provide tremendous data reduction, but in a highly nonlinear fashion. With this in mind, the primary goal of this paper is to show that for a reasonable class of problems the space dataP is a finite set of contractible, simplicial sets. The importance of this result is that it opens the possibility of applying standard algebraic topological tools, e.g., Lefschetz fixed point theorem, Conley index, to dynamics that is observed through the lens of persistent homology.

To state our goal precisely requires the introduction of notation. Throughout this paper SN denotes the 1-dimensional simplicial complex composed out of N vertices [i] (i=1,,N) and N-1 edges [i,i+1] (i=1,,N-1). It is a simplicial decomposition of closed bounded interval in R.

We study filtrations of SN defined as follows.

Definition 1.1

Let z=(z1,,zN)RN. Define f:RN×SNR by

f(z,σ):=zjifσ=[j],maxzj,zj+1ifσ=[j,j+1].

For rR, we set SN(z,r):=σSN:f(z,σ)r.

Definition 1.2

Given z=(z1,,zN)RN, we can reorder the coordinates of z such that

zj1zj2zjN.

The sublevel-set filtration of SN at z,1 which we write as SNF(z), is given by

SN(z,zj1)SN(z,zj2)SN(z,zjN).

Because SNF(z) is a finite filtration of simplicial complexes, completely determined by z, we can use classical results from (Edelsbrunner and Harer 2010; Zomorodian and Carlsson 2005) to compute the persistence diagram of SNF(z). We treat this as a map

Dgm:RNPer,

where Per denotes the space of all persistence diagrams. Thus the space dataP of all zRN having persistence diagram P is just Dgm-1(P). We remark that there are a variety of topologies that can be put on Per such that Dgm becomes a continuous map (Chazal et al. 2016; Cohen-Steiner et al. 2007).

Since SN is one-dimensional and contractible, we are only concerned with the persistent homology H0, i.e., the persistence diagrams associated with connected components. Therefore for the rest of the paper we restrict our study to consist of the family Per of persistence diagrams of level zero.

Here is the main result of this paper.

Theorem 1.3

For every persistence diagram P, the space dataPRN is composed of a finite number of mutually disjoint components. Each component is contractible, and is homeomorphic to a finite union of convex, potentially unbounded polytopes.

The proof of Theorem 1.3 is not particularly difficult, but it is technical. We first describe the connected components of dataP; see Lemma 2.4. In Sect. 2.2, we introduce the poset Str of cellular strings, which are be used to decompose each component as a finite union of convex polytopes in Sect. 2.3. In Sect. 3, we show that the realization of Str is contractible.

To emphasize that Theorem 1.3 is not a trivial result, we use Fig. 1 to demonstrate that dataP is not a convex subet of RN. In particular, consider the vectors v=(v1,,v4) and w=(w1,,w4) on the left of Fig. 1. It is left to the reader to check that Dgm(v)=Dgm(w) and that this persistence diagram is given by the pair of black dots (see right of Fig. 1). Note that the vectors in R4, indicated (on the left) in blue stars and red squares, lie on a straight line from v to w. However, the persistence diagrams indicated (on the right) in blue and red clearly differ from Dgm(v). Thus, the red and blue vectors do not lie in dataDgm(v).

Fig. 1.

Fig. 1

Non-convexity of the preimage dataP under the persistence map Dgm. In the left figure the two vectors, v=(v1,,v4) and w=(w1,,w4), lie in the preimage of the persistence diagram P, composed out of two black points visible on the right figure. Applying Dgm to convex linear combinations of v and w results in a path in the persistence plane illustrated on the right (the convex path is marked in grey, and two sample vectors on the path are marked using red squares and blue stars) (color figure online)

In Sect. 4 we apply Theorem 1.3 to prove the existence of fixed points with given persistence diagrams for a dissipative ordinary differential equation.

Invariants for a fixed persistence diagram

Fix a persistence diagram P. To describe the structure of the space dataP, we introduce two levels of invariants: the critical value sequences, representing the connected components of dataP, and (for each of these), a partially ordered set Str indexing a polytope decomposition of the component.

Components

Fix a persistence diagram P. To describe the (finitely many) connected components of dataP, it is useful to introduce notation that records the order in which the relevant local maxima and minima occur.

We say that z=(z1,,zN)RN is a typical point if its coordinates are distinct. If z is a typical point and 1<n<N, we say that zn is a local minimum (of z) if zn-1>zn<zn+1, and a local maximum if zn-1<zn>zn+1; it is a local extremum if it is a local minimum or maximum. We say that z1 and zN are boundary extrema; z1 is a local minimum (resp., maximum) if z1<z2 (resp., z1>z2).

Definition 2.1

The critical value sequence of a typical point z=(z1,,zN) is

cv(z)=zn1,,znKRK,

where the znk are the local extrema of z, excluding boundary extrema that are local maxima, and n1<n2<<nK.

Example 2.2

Let z=(1.5,-0.9,1.1,2.1,1.4)R5. The local minimum is z2 and the local maximum is z4. The boundary extrema are z1 and z5. Since z1 is also a local maximum we do not include it in the critical value sequence. Thus h=cv(z)=(-0.9,2.1,1.4).

The following notion emphasizes the structure of the critical value sequences.

Definition 2.3

A 010 critical value sequence of (odd) length K is a vector cv=(z1,,zK)RK with the property that

zn1<zn2>zn3<<znK-1>znK.

A 101 critical value sequence is defined similarly, with the inequalities reversed.

Since we are using sublevel set filtrations to compute the persistence diagram we focus on 010 critical value sequences.

Lemma 2.4 below shows that the local extrema of z are determined up to order by its persistence diagram, and hence that there are only finitely many critical value sequences for any fixed persistence diagram.

Recall that a persistence diagram is a finite collection of persistence points pi=(pib,pid), where pib and pid denote birth and death values, respectively. Since SN is connected, the persistence diagram of a typical point z has a unique persistence point pi=(pib,pid) such that pib=minn=1,,Nzn and pid=; without loss of generality, we may relabel pi as p1.

Lemma 2.4

Let zRN be a typical point with persistence diagram

pm=(pmb,pmd)m=1,,M.

Then, z has K=2M-1 local extrema; the local minima of z are precisely pmbm=1M and the interior local maxima of z are precisely pmdm=2M.

We leave the proof of Lemma 2.4 to the reader, remarking that it still holds when zRN is not a typical point, except that the persistence diagram may be a multiset (there may be multiple copies of a single persistence point).

Given a point z with persistence diagram P, let C(z) denote the component of dataP containing z.

The following lemma shows that dataP is the disjoint union of the finitely many disjoint components C(z), indexed by the critical value sequences. The proof follows from the observation that the order of the local extrema cannot be changed while preserving the persistence diagram.

Lemma 2.5

If z and z are typical points in RN then C(z)=C(z) if and only if cv(z)=cv(z).

Moreover, C(z) is the closure of the set of typical points in C(z).

This proves the first assertion in Theorem 1.3.

Remark 2.6

The components C(z) group vectors into equivalence classes that can be characterized using the notion of chiral merge tree as defined in Curry (2018). Corollary 5.5 of Curry (2018) shows that the number of chiral merge trees realizing diagram P is equal to 2N-1j=2NμB(Ij), where B is the barcode realization of P, i.e. set of intervals Ij=[bj,dj] having the birth and death values of the j-th persistence point as its endpoints, and μB(Ij) is the number of intervals in B that contain Ij.

Cellular strings

In this section, we define the poset Str(N,M) of cellular strings associated to M points arising from a vector in RN. Thus we fix N and M, where N2M-1.

Consider a string of symbols s=s1sN of length N, where each symbol sn is either 0, 1, or X (we refer to 0 and 1 as bits). Any such string can be represented as s=γ1γJ where each block γj is a substring made up of a single symbol (that is, γj is 00, 11, or XX), and consecutive blocks have different symbols. We refer to s=γ1γJ as the canonical representation of s.

Definition 2.7

Fix M<N. A 010 cellular string2 is a symbol string s of length N such that, for the canonical representation s=γ1γJ:

  • (i)

    the symbols that make up γj and γj+1 are different;

  • (ii)

    γ1 and γJ consist of the symbols 0 or X;

  • (iii)

    if γj consists of the symbol X, then the symbol of γj-1 is different from the symbol of γj+1;

  • (iv)

    there are exactly M values of j for which γj consists of the symbol 0.

The set Str(N,M) of cellular strings is a poset, where s<s if the string s is obtained from s by replacing some of the bits 0 and 1 in s by X.

The dimension of a cellular string s, dim(s), is the number of symbols X in s. It follows from (iv) that M of the blocks γj have the form 00, and M-1 have the form 11. Thus, K=2M-1 of the blocks are bitstrings. If these bitstrings are γj1,,γjK, then the symbol for γjk is 0 if k is odd and 1 if k is even. Since each block has at least one symbol, it follows that any cellular string has dimension at most L=N-K.

We write Str(r)(N,M) for the sub-poset of all cellular strings whose first r-1 symbols are X. Note that Str(N,M)=Str(1)(N,M) and Str(L+1)(N,M)={XX01010}.

Proposition 2.8

An element of Str(NM) is maximal if and only if it is an L-dimensional cellular string, where L=(N-K).

Proof

Let s=γ1γJStr(N,M). By definition, dim(s)L. Conversely, suppose that the symbol X appears in s has less than L times. Then some bitstring γj has length 2. Let s be the cellular string obtained by replacing the first symbol of γj by X. Then s<s, so s is not maximal.

Since both N and M are fixed in our analysis, we simplify the notation and write Str for Str(N,M). Figure 2 illustrates the poset Str when M=2, K=3 and N=5; the right column is Str(2).

Fig. 2.

Fig. 2

The string poset Str for M=2 and N=5. Two-dimensional, one-dimensional, and zero-dimensional strings are surrounded by rectangles, ellipses, and nothing, respectively. The arrows indicate the partial order. The rightmost column is the sub-poset Str(2)

Lemma 2.9

Every string s is the greatest lower bound of the set of L-dimensional strings s with s<s.

It follows that Str has the least upper bound property: if two strings have a lower bound, they have a greatest lower bound.

Proof

We proceed by downward induction on the dimension d of s, the case d=L being clear. Consider the canonical representation, s=γ1γJ. If d<L, then some bitstring γj has length 2. Consider the strings s1=γ1γj-1Xγ¯γj+1γJ and s2=γ1γj-1γ¯Xγj+1γJ where γ¯ is a bitstring consisting of the same symbol as γj but of length one less than γj. Since this is the form of any cellular string s satisfying s<s and dims=dims+1, the result follows.

Let s be an L-dimensional cellular string. Successively replacing an X adjacent to a bit (0 or 1) by that bit yields a chain of strings s=sL>sL-1>>s1>s0. It follows that every maximal chain in the poset has length L.

Example 2.10

Consider a string s(n)=σ10XX1σ2 with a block of n consecutive X’s (where σ1 and σ2 are fixed substrings). Let Str/s(n) denote the sub-poset of Str consisting of all strings ss(n) which begin in σ10 and end in 1σ2. Then Str/s(n) is isomorphic to the poset In of integer intervals [ij] with 1ijn+1. (The string corresponding to [ij] is

σ100XX11σ2;

it has i 0’s and the first 1 is in the (j+1)st spot.)

If s is a cellular string with k blocks of successive X’s (of lengths n1,,nk), the sub-poset Str/s of strings s<s in Str is isomorphic to the product of posets Str/s(n1),,Str/s(nk), i.e., to the poset

In1××Ink.

The polytopes

We now turn to identifying the polytopes of Theorem 1.3. Fix a 010 critical value sequence cv=zn1,,znK as in Definition 2.3. To each d-dimensional cellular string s we assign a d-dimensional polytope T(s) in RN; T(s) will be a product of simplices.

Let s=γ1γ2γJ be the canonical representation of a string s, as in Definition 2.7. Let nj denote the length of the substring γj, so N=nj.

  • If γj is either 00 or 11, and γj is the kth block from the left involving 0 or 1, we set
    T(γj)=zknk=(zk,,zk).
  • If γ1 is a block XX, then
    T(γ1)=(x1,,xnj)Rnj:x1xn1z1.
  • If γJ is a block XX, then
    T(γJ)=(x1,,xnj)Rnj:zkx1xn1.
  • If γj is a block XX (for 1<j<J), and γj-1 is the kth block from the left involving 0 or 1, then
    T(γj)=(x1,,xnj)Rnjwherezkx1xnjzk+1ifkis odd;zkx1xnjzk+1ifkis even.
  • We define T(s)RN to be the concatenation:
    T(s)=T(γ1γ2γJ)=j=1JT(γj).

Let P be a persistence diagram and zdgm-1(P). The component C(z) of dataP is the union of the T(s), where sStr and T(s) is defined using the critical value sequence cv(z). This is clear from Definition 2.1.

Since the critical value sequence is always assumed to be fixed, we will suppress it in the notation.

Example 2.11

Consider the case K=3 and N=5. If s=01XX0, then (γ1,,γ4)=(0,1,XX,0). So, (n1,n2,n3,n4)=(1,1,2,1) and hence

T(01XX0)=z1×z2×(x1,x2):z2x1x2z3×z3Δ0×Δ0×Δ2×Δ0.

If s=X01X0, then (γ1,,γ4,γ5)=(x,0,1,x,0). So, (n1,n2,n3,n4,n5)=(1,1,1,1,1) and hence

T(X01X0)=[z1,)×z1×z2×[z3,z2]×z3[0,)×Δ0×Δ0×Δ1×Δ0

Similarly, T(X0100)=[z1,)×z1×z2×z3×z3[0,)×Δ0×Δ0×Δ0×Δ0.

Observe that X0100<X01X0 and T(X0100)T(X01X0).

Let Poly denote the poset of polytopes in RN under inclusion. By definition, T maps strings in Str to polytopes in Poly.

Lemma 2.12

T :  StrPoly is an injective poset morphism, and preserves greatest lower bounds.

Proof

Suppose that s<s and 1+dims=dims. If s=γ1γJ is the canonical form, then some γj has the form aa (where a is 0 or 1), and s has the form

s1=γ1γ¯jXγJors2=γ1Xγ¯jγJ,

where γ¯j=aa has one fewer bit that γj. It is clear from the definition of T that T(s1)T(s2), and T(s) is the intersection of T(s1) and T(s2), as desired.

Geometric realization of posets

Let C be a poset (partially ordered set). For any cC, we write C/c for the sub-poset c:cc; C is the union of the C/c. If c1 and c2 have a greatest lower bound c12, then (C/c1)(C/c2)=C/c12.

By definition, the geometric realization BC of any poset C is a simplicial complex whose k-dimensional simplices are indexed by the chains c0<c1<ck of length k in C. It is the union of the realizations B(C/c) of the sub-posets C/c; if c1 and c2 have a greatest lower bound c12, then B(C/c1) and B(C/c2) intersect in B(C/c12). See Weibel (2013, IV.3.1) for more details.

Here are some basic facts; see Weibel (2013, IV.3) for a discussion. A poset morphism f:CC determines a continuous map BCBC, and a natural transformation η:ff between morphisms gives a homotopy Bη:BCBC between f and f. In addition, realization commutes with products: B(C1×C2)(BC1)×(BC2). Applying these considerations to the poset Str, we see that its realization BStr is the union of the polytopes B(Str/s), and if s12 is the greatest lower bound of s1 and s2 then B(Str/s1)B(Str/s2) is B(Str/s12).

Let s be a cellular string. We saw in Example 2.10 that the poset Str/s is isomorphic to the product In1××Ink of the posets Inj of integer intervals in [1,nj+1], corresponding to the blocks of nj succesive X’s in s. It is well known that B(In) is homeomorphic to the n-simplex Δn. Thus

B(Str/s)B(Inj)Δn1××Δnk.

By construction, T(s)=T(γj) also has this form. Hence we have a natural homeomorphism

B(Str/s)B(Str/s(nj))B(Inj)T(γj)=T(s).

Theorem 2.13

B Str is homeomorphic to C(z).

Proof

By construction, C(z)=T(s), and BStr=B(Str/s). It suffices to observe that for each s1,,sn the restriction of the BStr/siT(si) induces a homeomorphism between the intersection of the B(Str/si) and the intersection T(si). This holds because the two sides are identified with B(Str/s) and T(s), where s is the greatest lower bound of the si.

Contractibility

We now define a poset morphism F1:StrStr, and modify it to define poset morphisms F:Str()Str() for >1.

Definition 3.1

Let s be an L-dimensional cellular string. We define F1(s) to be the string obtained from s by transposing the first (i.e., leftmost) X with the bit immediately preceding it. If X is the initial symbol, we set F1(s)=s.

If s is a lower-dimensional cellular string, we define F1(s) as follows. If s has an initial X with no 00 or 11 preceding it, we do as before: transpose X with the bit immediately preceding it, or do nothing if X is the initial symbol. If s begins with a block of n+1 zeroes, say s=000σ2, we replace the initial 0 by X, so F1(s)=X00σ2. Otherwise, the string must have the form s=σ1abbσ2, where ab are bits, ab, σ1 is an (alternating) bitstring not ending in a, and σ2 is the remainder of the string. We set

F1(s)=σ1aabσ2.

The definition of F:Str()Str() mimics that of F1. Specifically, if s=βσ, where β=XX is a block of length -1 then F(s)=βF1(σ).

Example 3.2

In Fig. 2, the map F1 sends strings surrounded by rectangles (resp., ellipses) from one column to strings surrounded by rectangles (resp., ellipses) in the second column to the right, while leaving the last column fixed. Thus F1(01100)=00100 and F1(00100)=X0100.

Since Str(2) is the rightmost column, the map F2 acts on this column, mapping strings surrounded by rectangles (resp., ellipses) to those two rows down. Thus F2(XX010)=XX010, F2(X0010)=XX010, and F2(XX010)=XX010.

Lemma 3.3

F1: StrStr is a poset morphism, and is the identity on the sub-poset Str(2).

Furthermore, F1K(Str)=Str(2).

Proof

We proceed by downward induction on d=dim(s) to show that if s<s then F1(s)F1(s). If s contains an x with no 00 or 11 preceeding it, the same is true for s and the inequality is evident.

Next, suppose that s=σ1abbbσ2, where σ1a is an alternating bitstring. If s=σ1abbbσ2 for some σ2σ2 then

F(s)=σ1aabbσ2<F(s)=σ1aabbσ2.

For s1=σ1aXb and s2=σ1abbXσ2, we also have F(s)<F(s1) and F(s)<F(s2). Otherwise, either s1<s or s2<s; in these cases, F1(s1)F1(s) or F1(s2)F1(s), by induction, and hence F(s)<F(s).

Finally, if s=000σ then either s1=X00σs or else s2=000Xσs. By induction, F1(s1)F1(s) or F1(s2)F1(s), so it suffices to observe that F1(s)F1(s1),F1(s2).

Remark 3.4

The proof of Lemma 3.3 also shows that each F is a poset morphism.

We can filter the poset Str by sub-posets Fili, where Fil0=Str(2), FilK=Str and Fili is the full poset on the set of strings s with F1i(s)Str(2). In Fig. 2, for example, Fil1 (resp., Fil2) is the rightmost 3 columns (resp., 5 columns). Since F1 maps Fili to Fili-1, the geometric realization of BF1 restricts to a continuous map from BFili to BFili-1. We will prove:

Proposition 3.5

The inclusions BFili-1BFili are homotopy equivalences. Hence B Str(2)B Str is a homotopy equivalence.

Proof

For i>0, we define poset morphisms F1,i:FiliFili-1Fili to be the identity on Fili-1 and F1 otherwise. The geometric realization of F1,i is a continuous map BFiliBFili-1BFili which is the identity on BFili-1.

We will prove that, on geometric realization, BF1,i is homotopic to the identity on BFili.

We define a poset morphism h:FiliFili as follows. If sFili-1 then h(s)=s; if sFili-1, define h(s) to be the greatest lower bound of s and F1(s). Thus Bh is a continuous map from BFili to itself. For sFili, the inequalities sh(s)F1,i(s) yield natural transformations idihF1. and hence homotopies between the maps idi (the identity map on BFili), Bh and BF1,i.

Corollary 3.6

Each B Str(+1)B Str() is a homotopy equivalence. In particular, the inclusion of the point B Str(L+1) in B Str is a homotopy equivalence, i.e., BStr is contractible.

Remark 3.7

We can describe the map T(s)T(F1(s)) induced by F1. For example, suppose that s=σ1γj-1γjσ2, where σ1=γ1γj-1 is an alternating bitstring of length 2 and γj is a block XX. Then T(γj-1)={zj-1} and T(γj)Rnj is defined by inequalities, either zj-1x1 or zj-1x1, depending on the parity of j. The map F1 sends T(γj-1)×T(γj) to the subset

T(X)×{zj-1}×T(γ),

where T(X) is defined by zj-2x1zj-1 and T(γ) is defined by the equations zj-1x2 or zj-2x1. In effect, the map sends x1 to zj-1.

Existence of fixed points for flows

As an application of Theorem 1.3, we establish the existence of a fixed point solution of a ordinary differential equation whose trajectories are being observed in the space of persistence diagrams. To be more precise consider a differential equation z˙=f(z), zRN, with the property that it possesses a compact global attractor A (Raugel 2002). Given an initial condition z(0)=z¯RN, we write z(t)=φ(t,z¯), t[0,) for the solution in forward time. The important consequence of the existence of a compact global attractor is that there exists R>0 such that for any initial condition z¯ there exists tz¯>0 such that φ(t,z¯)<R for all ttz¯. We say that R is a bound for A. Observing the persistence diagrams along a trajectory results in a curve Dgm(φ(t,z¯))Per. In what follows we do not assume that we have knowledge of the nonlinearity of f, or of the actual trajectories φ(t,z); we are only given the curves Dgm(φ(t,z¯)) of persistence diagrams.

Even if the persistence diagram is constant, we cannot conclude that the underlying differential equation has a fixed point. As an example, consider a differential equation in R3 with a periodic solution in which the first coordinate z1=0 is constant, and (z2,z3) oscillates with the property that 1z2z3. The associated curve in Per consists of the constant persistence diagram P=(0,).

However, Theorem 4.3 provides a scenario under which the observation of sufficiently many trajectories suggests the existence of a fixed point for the unknown ordinary differential equation that generates the dynamics. More general theorems are possible and, as will be discussed in a later paper, these techniques can be lifted to the setting of partial differential equations defined on bounded intervals. The purpose of this example is to emphasize the importance of Theorem 1.3 from the perspective of data analysis. Thus, we focus on a much more modest result. We will show that if a particular type of neighborhood in Per is positively invariant under the dynamics, i.e. if Dgm(z) is in the neighborhood implies that Dgm(φ(t,z)) is in the neighborhood for all t>0, then there exists a fixed point for the differential equation that generates the dynamics. To state and obtain such a result requires the introduction of additional notation.

Definition 4.1

We shall say that a persistence diagram

P=pm=(pmb,pmd):m=1,,M

is sparse if each persistence point is unique, i.e. pmpn for all mn.

Given a sparse persistence diagram we can choose μ>0 such that pm-pn4μ for all mn and |pmd-pmb|4μ for all m.

Example 4.2

A sparse persistence diagram Q is shown in Fig. 3. We can choose μ=0.25. A possible critical value sequence associated to Q is cv(z)=(3,4.5,1,3.5,2).

Fig. 3.

Fig. 3

A sparse persistence diagram Q with persistence points (1,),(2,3.5),(3,4.5). The boxes indicate the set NQ for μ=0.25

We use μ to define subsets of RN and Per. We begin by constructing a subset of RN using the set of cellular strings Str(N,M). Choose a point z^ with persistence diagram P. This gives rise to a fixed critical value sequence cv(z^) and the associated component C(z^)RN of dataP is given by

C(z^)=sStr(N,M)T(s).

By Theorem 1.3, C(z^) is a contractible union of polytopes.

Let Bμ(C(z^))RN be the set of points that lie within a distance μ of C(z^) using the sup-norm. The bound on the choice of μ guarantees that if s,sStr(N,M) are of maximal dimension and there does not exist sStr(N,M) such that s<s and s<s, then Bμ(T(s)) and Bμ(T(s)) are disjoint. Therefore Bμ(C(z^)) is contractible.

We now turn to the subset of Per. For each m=1,,M set

Pm:=p=(pb,pd):p-pm1μ

and

D:=p=(pb,pd):pb[p1b-μ,R]and0pd-pbμ

for some R>sup{pmb}+μ. See Fig. 3. Define NPPer to be the set of persistence diagrams generated by elements of RN with the property that for each m=1,,M there exists a unique persistence point in Pm and any other persistence points lie in D.

These constructions allow us to prove the following theorem concerning the existence of fixed points of the unknown, underlying dynamical system φ.

Theorem 4.3

Consider a dynamical system generated by an ordinary differential equation that has a global compact attractor A with a bound R, and whose trajectories are represented by φ(t,z). Let P be a sparse persistence diagram and let NPPer be defined as above. Assume that if Dgm(z)NP, then Dgm(φ(t,z))NP for all t0.

Then, for each component of Dgm-1(NP)RN there exists a vector z^ such that Dgm(z^)NP and φ(t,z^)=z^ for all tR, i.e. z^ is a fixed point for the dynamical system.

Proof

We begin with the observation that if zBμ(C(z^)) and there exists t1>0 such that φ(t1,z)Bμ(C(z^)), then there exists t0(0,t1] such that Dgm(φ(t0,z))NP. This follows from the stability theorem of persistent homology using the bottleneck distance (Cohen-Steiner et al. 2007). This contradicts the hypothesis, therefore, that Bμ(C(z^)) is a contractible, positively invariant region under the dynamics. By McCord and Mischaikow (1996, Proposition 3.1) the Conley index of the maximal invariant set is that of a hyperbolic attracting fixed point. By McCord (1988, Corollary 5.8) (which utilizes the well known Lefschetz fixed point theorem), the maximal invariant set in Bμ(C(z^)) contains a fixed point.

Conclusion and future work

Recall from Remark 2.6 that Curry (2018) provides a count of the contractible components of the preimage of a persistence map. However, to the best of our knowledge, this paper provides the first detailed analysis of the homotopy type of these of a components. Although we have presented the results in the context of sublevel set filtrations, the same arguments can be applied in the setting of superlevel set filtrations. The only significant change is that one needs to use 101 cellular strings; see Definitions 2.3 and 2.7.

Theorem 4.3, and the use of persistence diagrams to obtain results about the dynamics of an ODE, may appear somewhat artificial. However, consider a PDE, such as a reaction diffusion equation, defined on an interval. A finite spatial sampling of the solution at a time point gives rise to a vector. We can think of this vector as arising from two different proceedures: (i) numerical, e.g. the values of an ODE derived from a Galerkin approximation to the PDE, or (ii) experimental, e.g. a pixelated image of the solution. Theorem 4.3 is applicable in both cases, and one expects that for fine enough discretization or resolution that the results of Theorem 4.3 will be applicable to the PDE. The example involving images brings us much closer to current treatments of complex spatio-temporal dynamics (Kramar et al. 2016; Levanger et al. 2019). Hence, the natural next step in our research is to obtain an analogous result about existence of fixed points for one-dimensional PDEs whose trajectories are observed in the persistence space.

Finally, the obvious open question as a result of this paper is: given a d-dimensional simplicial complex S with a function f, similar in form to that of Definition 1.1, can one determine the homology of components of the pre-image of a persistence diagram?

Acknowledgements

The work of JC and KM was partially supported by Grants NSF-DMS-1125174, 1248071, 1521771 and a DARPA contract HR0011-16-2-0033. CW was supported by NSF grants 1702233 and 2001417. In addition KM was partially supported by DARPA contract FA8750-17-C-0054, NIH Grant R01 GM126555-01, and NSF Grants 1934924, 1839294, 1622401, and the NSF HDR TRIPODS award CCF-1934924. In addition JC was partially supported by NAWA Polish Returns Grant PPN/PPO/2018/1/00029.

The first two authors are grateful to an anonymous reviewer of a dramatically different version of this paper for suggesting the relation of our efforts to K-theory, which greatly simplified the proof of Theorem 1.3.

Footnotes

1

Analogous results can be obtain for superlevel set filtrations (see Sect. 5).

2

A 101 cellular string is defined similarly, interchanging 0 and 1.

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Contributor Information

Jacek Cyranka, Email: jcyranka@gmail.com.

Konstantin Mischaikow, Email: mischaik@math.rutgers.edu.

Charles Weibel, Email: weibel@math.rutgers.edu.

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