Huck Stepanyantsstepanyants.j@northeastern.eduDepartment of Physics, Northeastern University, Boston, Massachusetts 02115, USANetwork Science Institute, Northeastern University, Boston, Massachusetts 02115, USAAlan BeardonDepartment of Pure Mathematics and Mathematical Statistics, University of Cambridge, Cambridge CB3 0WB, UKJeremy PatonDepartment of Physics, Northeastern University, Boston, Massachusetts 02115, USANetwork Science Institute, Northeastern University, Boston, Massachusetts 02115, USADmitri KrioukovDepartment of Physics, Northeastern University, Boston, Massachusetts 02115, USANetwork Science Institute, Northeastern University, Boston, Massachusetts 02115, USADepartment of Mathematics, Northeastern University, Boston, Massachusetts 02115, USADepartment of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts 02115, USA
Abstract
Riemann surfaces are among the simplest and most basic geometric objects. They appear as key players in many branches of physics, mathematics, and other sciences. Despite their widespread significance, how to compute distances between pairs of points on compact Riemann surfaces is surprisingly unknown, unless the surface is a sphere or a torus. This is because on higher-genus surfaces, the distance formula involves an infimum over infinitely many terms, so it cannot be evaluated in practice. Here we derive a computable distance formula for a broad class of Riemann surfaces. The formula reduces the infimum to a minimum over an explicit set consisting of finitely many terms. We also develop a distance computation algorithm, which cannot be expressed as a formula, but which is more computationally efficient on surfaces with high genuses. We illustrate both the formula and the algorithm in application to generalized Bolza surfaces, which are a particular class of highly symmetric compact Riemann surfaces of any genus greater than1.
I Introduction
Riemann surfacesForster (1981) are one of the simplest geometric objects, with widespreadapplications in physics, mathematics, network science, and other areas. In physics, Riemann surfaces appear naturallyin dynamical systems. The simplest example is billiardsMasur (1986); Gutkin (2012), i.e.,a freely moving ball confined to a polygon-shaped table, and bouncing off its sides.This system is equivalently formulated as a particlemoving along a straight line on a compact Riemann surface. In particular, a particle moving on the Bolzasurface, a compact Riemann surface of genus, is one of the simplest and earliest chaotic systemsstudied in physicsHadamard (1898).
Riemann surfaces also appear in integrable systemsBabelonetal. (2003); Gieres (1993).The trajectory of such a system in its parameter space is effectively confined to a Riemann surface.Elsewhere in mathematical physics, Riemann surfaces are a part of string theoryKimetal. (2018); Bonellietal. (2000),potential theoryGustafssonandTkachev (2009); Chirka (2018), and approximation theoryKomlovetal. (2017); Aptekarevetal. (2014); AptekarevandLysov (2010).In quantum physics, Riemann surfaces appear in the study of fractional quantum Hall statesWenandNiu (1990),spin liquidsWen (1991), and quantum gravityAldrovandiandTakhtajan (1997); DistlerandKawai (1989).
In network science, Riemann surfaces have been used as latent spaces in some models of networks.For example, networks embedded in hyperbolic spaces are one of the first models that reproduce acollection of the most basic properties common to many real-world networksKrioukovetal. (2010).Yet our main motivation for this paper comes fromvander Hoornetal. (2021); Hoornetal. (2023), where the convergence of theOllivier curvatureOllivier (2007) of random geometric graphsPenrose (2003); DallandChristensen (2002) to the Ricci curvature of theirunderlying Riemannian manifolds was proven. Vertices in such graphs are random points in a Riemannianmanifold, and pairs of vertices are connected by edges if the distance between the two vertices in themanifold is below a fixed threshold. To build a random geometric graph in a manifold, one thus needs tobe able to compute distances between pairs of points in it.
Compact Riemann surfaces are two-dimensional Riemannian manifolds. They can be equippedwith a metric which defines the distance between a pair of points on the surface.Genus (sphere), (torus), and surfaces admit the spherical, euclidean, andhyperbolic metrics, respectively. While computing distances on the first two of these surfacesis trivial, the task is much more difficult on a hyperbolic Riemann surface,where the distance is given as an infimum over a countably infinite number of geodesics,i.e., locally minimal paths. There are no formulas for the distance between pointson any genus surface which are computable in finite time. To be precise, while it is knownthat the mentioned infimum is actually a minimum since only a finite set of geodesics needs to beconsidered (Prop.2.1,LuandMeng (2020)), it is not known what this set actually is forany surface, which is surprising since distance plays a crucial role inmany applications of these surfaces mentioned above.
Here we compute distances on Riemann surfaces obtained by pairingthe sides of a convex hyperbolic polygon. We obtain an expression for a finite set of geodesicswhose minimum gives the correct distance between any two points on these surfaces. This leads to a distance formulawhich can be evaluated in finite time. We also develop a fast algorithm for computing distances on thesesurfaces using a different set of ideas. For illustrative purposes, we show how the formula and the algorithm can be appliedto computing distances on generalized Bolza surfacesEbbensetal. (2022). These are highly symmetric hyperbolic Riemann surfacesof any genus. We prove that the time required to evaluate the formula in this case is,where, and show experimentally that the time required to run the algorithm is.
In Sec.II, we provide necessary background information on Riemann surfaces, and a detailed introduction to the distance problem.In Sec.III andIV, we present and discuss our results, a formula and an algorithm, respectively,for computing distances on Riemann surfaces. In Sec.V, we apply our results togeneralized Bolza surfaces, and evaluate the resulting running times.
II Background and definitions
The distance between two points in a space is the length of the shortest geodesic, or locally minimal path,that connects them in the space. If the space is a compact Riemann surface, then the number of geodesics connecting a given pair of points dependson its genus, which is the number of holes the surface has. Given two points on the genus sphere (Fig.1(a)), there arealways two geodesics between them, and thus the distance is simply a minimum of two lengths. In contrast, the number of geodesics between twopoints on the torus is infinite (four are shown in Fig.1(b)),which could make computingdistances on the torus difficult. Nevertheless, due to its symmetry, the distance on the torus is given by the simple formula:
(1)
where and are the coordinates of the representatives of two points on the torus, whichis the unit square with vertices at whose opposite sides are paired, Fig.1(c). For genus (hyperbolic) surfaces,the number of geodesic paths connecting two points is also infinite. Yet there are no closed-form distanceformulas analogous to Eq.(1) for any of these surfaces.
Here, we address the distance problem for the hyperbolic Riemann surfaces known as quotient surfaces.A quotient surface is constructed by taking the quotient of the hyperbolic plane with respect to a discretesubgroup of its isometries, or distance-preserving transformations, called a Fuchsian group, which we describe next.
We will use the Poincaré disk model of, which is thecomplex unit disk equipped with the metric
(2)
The group of orientation-preserving isometries of the Poincaré disk is the projective specialunitary group, the quotient of the special unitary group byits center. In matrix form, these isometries are
(3)
which act on points via fractional linear transformations,
(4)
A subgroup of the isometry groupis called Fuchsian if it acts discontinuouslyon. This means that for any point, the orbit has no accumulationpoints. An accumulation point is one for which there are elements ofin any arbitrarily small punctured disk around. Every Fuchsian group defines a quotientsurface,
(5)
Points on are orbits of points in under actions by,
(6)
The distance between two points and on isa distance between orbits and in. It is given by
(7)
where is the distance in.This formula cannot be directly evaluated, since has infinitely manyelements. To find, one must consider infinitely many geodesics fromto. Every such geodesic corresponds to a different minimal path between thesame pair of points on, like those illustrated in Fig.1 for the sphere and torus,or in Fig.6 for the Bolza surface.
Here we will focus on surfaces whose Fuchsian groups are finitely generated andof the first kind. The latter means that any point on the boundary of the unit disk, which is theboundary at infinity of the hyperbolic plane, is an accumulation point of some orbitof. This guarantees that is a compact surface, and byTheorem10.1.2 inBeardon (2012),has a convex fundamental polygon with a finite number of sides.A fundamental polygon is one that containsexactly one representative of each point on, see Fig.2. It is knownBeardon (2012) thatin such settings, the generators of the group pair sides of by mapping itto its edge-adjacent neighbors, shown in light grayin Fig.2. We will further assume that does not have any vertices at infinity.
Using the geometry of, we develop two methods for computing distances on.In the next Sec.III, we present a distance formula which reduces theinfinities in Eq.(7) to finite sets. Then in Sec.IV, we developa more efficient algorithmic approach to the problem.
III Distance formula
Let be a quotient surface obtained by pairing the sides of a convex fundamentalpolygon, and let denote its Fuchsian group. Let and berepresentatives of two points on the surface. In this section we obtain acomputable version of Eq.(7), where instead of minimizingover all, we minimize over a finite subset.We will derive a formula for using the geometry of.
Let us call a pair of images of neighbors if they share either a common edge or a common vertex.Let denote the subset of isometries from the Fuchsian group which mapto all of its neighbors and itself. Then we define theshell,for, to be the difference
(8)
and for, we define, as illustrated in Fig.3.
We next compute an upper bound on the minimum value of,, for which the correct valueof the distance is obtained by minimizingover. Thus is the minimum positive integer satisfying
(9)
for all and. In other words, the finite subset mentioned abovecan be taken to be
(10)
We bound for an arbitrary surface by the ratio of two distances: the surfacediameter, i.e., the maximum possible distance between two points on the surface, andthe minimal distance required to traverse a shell.To traverse here means to cross from the outer boundary of to the outerboundary of for. In AppendixA, we show that this distance does not dependon, and satisfies the inequality
(11)
where is the minimum distance between any pair of non-adjacent sides of, and isthe minimum distance from the midpoint of an side to any adjacent side as illustrated inFig.4.
Then the distance required to cross from a starting point in to a pointin is lower-bounded by. Since is the surface diameter,then must satisfy the inequality
(12)
But the surface diameter is trivially upper bounded by the diameterof its fundamental polygon,:
(13)
where the are the vertices of.Combining Eqs.(11),(12), and(13) leadsto an upper bound on in terms of geometric properties of, which we denote:
(14)
Substituting this bound in Eq.(9) yields an explicit computable formula for the distance betweena pair of points on the surface:
(15)
IV Distance algorithm
In this section, we present an algorithm to computeby minimizing over an even smaller subset of. This subset depends on the locationof and is unknown a priori—instead, it is constructed via a search over thepolygon tessellation. As we will see in Sec.V, this method is generally more computationallyefficient than evaluating the distance formula in Sec.III.
Let be the number of sides of, and be the generators ofand their inverses. With these notations, the algorithm is:
This algorithm is a depth-first search for an image of,, that minimizes, in the polygon tessellationillustrated in Fig.5.We keep track of two variables: the list of isometries correspondingto the polygons to be searched next, and the current best solutionthat minimizes across all searched so far.Both and are initialized to theidentity:, corresponding to itself.
At every step of the search, we set to be the last elementof, and remove it from.If, we updateto. We then append to the group elements,,corresponding to polygons that are edge-adjacent to the currently searched polygon, but only if theyare at a greater distance from:
(16)
Here, the distances and fromto the polygons and are defined to be the minimum distancefrom to one of their sides, with the exception.
The reason to consider only those polygons that increase the distance from is as follows.Consider the shortest path from to, where. It intersects an edge-adjacentpolygon of. This edge-adjacent polygon is closer to than. Continuing inthis way, we construct a chain of edge-adjacent polygons leading from to along which thedistance to is decreasing, which guarantees that will be reached in the search.
The reason not to search the polygons forwhich exceeds is this:
(17)
where is the group element we are looking for, i.e., the one thatminimizes, and is the surface diameter.
Finally, the condition in Eq.(16) ensures that the search terminatesafter a finite time. To see this, let denote the number of polygons within thedistance from. Note that the number of branches of the searchcannot exceed the number of sequences of such polygons ordered by increasing distance, whichis. Therefore, the number of polygons searched does not exceed.We also consider a total of nearest neighbors of at every search step, eventhough not all of these neighbors are added to. Therefore the total number of steps in thealgorithm is. In practice, however, we find that the number of stepsis, implying that most polygons are searched only times, as discussed in the nextsection.
V Application to Generalized Bolza Surfaces
In this section, we apply our general results to the generalized Bolza surfacesEbbensetal. (2022),highly symmetric surfaces of any genus. We denote them by. is known as the Bolza surfaceBolza (1887).
The generalized Bolza surface has as its fundamental polygon the regular-gon withinterior angles and vertices
(18)
where is the radius of, which satisfies
(19)
The Fuchsian group of is generated by the following isometries(Fig.6), which pair opposite sides of the fundamental polygon:
(20)
where is the length of a side of, which satisfies
(21)
Applying the distance formula to these surfaces, we observe that(cf. Fig.4), and then upper boundusing hyperbolic trigonometry to obtain
(22)
The diameter of is knownStepanyantsetal. (2024),
(23)
and therefore we can substitute the actual diameter, instead the upper bound inEq.(13), into Eq.(14) for to obtain
(24)
The number of polygons in is upper bounded by the number of sequences ofgenerators,
(25)
and since for large, the time required to evaluate the distanceformula is,where.
For the Bolza surface, evaluating Eq.(24) yields, but in AppendixBwe compute exactly using different methods:
(26)
Therefore, the number of’s images one must search through to compute the distanceis, corresponding to all polygons adjacent to via either aside or a vertex.
To apply the distance algorithm, we substitute the diameter of the surface forin Algorithm1, and find experimentally in Fig.7 that the algorithmrunning time is. We cannotprove this observation because we cannot estimate the overcount—the average number of times the samepolygon is searched. However, we can show that a maximum ofpolygons can intersect a hyperbolic circle of radius. Therefore, Fig.7suggests that each polygon is searched times, so overcounting appears to be minimal.
Appendix A Lower bound on
In this section, we prove an upper bound on the shell-crossing distancefor any quotient surface obtained by pairing the sides of a convex polygon.
This distance is
where is the shell, is the set consisting of all maps from toan edge- or vertex-adjacent polygon and the identity, and is the boundary of.
First, observe that for we have
Hence,
Since is the minimum of the right hand side over all and, and the left hand side isequal to, which is by definition of,we conclude that
Next, we introduce edge labels, which are positive integers modulo the numberof distinct (in the quotient sense) sides of. First, the label is assignedto an edge of without loss of generality. Then, the edges to the left ofthis edge are labeled (Fig.8). Finally, these labels are copied onto allimages of via the elements of the Fuchsian group. Thus, edges are distinct inthe quotient sense if and only if they have different labels. The process of filling in labels, whichwe call label chasing, will be useful later.
Now we will prove the main result of this section:
(27)
where denotes the distance between edges and which are distinct andnonadjacent, and denotes theminimal distance from the midpoint of edge to either of the adjacent edges.
Starting once more with arbitrary points and, webreak into smaller segments so that each segment is contained in a polygonand has both endpoints on its boundary. For such a segment, suppose the endpoints lie on edges withlabels. We categorize a segment (see Fig.8) as
1.
type I if,
2.
type II if, and
3.
type III if.
The segments cannot be all typeI, since this would imply that circles around avertex indefinitely without ever reaching, which cannot happen. Similarly,the segments cannot all be typeII. This leaves two cases: there is either (1) aconsecutive pair of typeI and typeII segments in either order, or (2) a typeIII segment.In the first case, we can show using elementary geometry thata lower bound on the combined length of the typeI and typeII segments, yielding a lower boundon, is
(28)
In the second case, is lower bounded by the minimum length of a typeIII segment,
(29)
Therefore, in either case, Eq.(27) holds.
Appendix B for the Bolza surface
In this section, we prove that for the Bolza surface.
For an arbitrary point, it suffices to show that for any point not in thefirst shell, defined in Sec.III, thereexists a group element such that. Equivalently, this meansthat the Dirichlet polygon of is contained inside.
We prove this by contradiction.Assume that there exist points that contradict the aboveassertion: for all. This meansthat is either inside the Dirichlet polygon of or on its boundary.Using the argument from AppendixA, thegeodesic must contain either
Case (1):
a segment composed of a pair of typeI and typeII segments in either order, or
Case (2):
a typeIII segment.
Denote the endpoints of this segment such that is closer tothan to. Since the Dirichlet polygon of is convex, contains and,and are on a line, must bein the interior of the Dirichlet polygon of. This implies that is in the interiorof the Dirichlet polygon of, and so by the same argument must lie in the interior ofthe Dirichlet polygon of. This means that
(30)
Consider now case(1) above, where and are endpoints of a typeI segment joined to a typeIIsegment (Fig.9). Assume without loss of generality that is on side. Letand. Label-chasing, we find that there must be an image oflocated on the same side as. Applying the triangleinequality to triangles and, whereis the intersection between and, gives
(31)
which is a contradiction with Eq.(30).
In case(2), points and are endpoints of a typeIII segment, so they lie on sides of the samepolygon. Consider the imagesof located on the opposite sides of the polygon, and draw the perpendicular bisectorsof and (Fig.10).Observe that must be closer to than to (on the same side of the vertical perpendicularbisector as), and must be closer to than to (on the same side of the horizontalperpendicular bisector as). This is only possible when and are exactly 2 edges apart and lie inthe same quarter-polygon, as shown in Fig.10.
Next, assume without loss of generality that is on side (Fig.11), and uselabel-chasing to determine the location of another image of, denoted.Let and. From Fig.10, we have.Applying hyperbolic trigonometry to quadrilateral and righttriangle, one can show that
(32)
(33)
Rearranging the first equation gives
(34)
where is a function of and only. The coefficient in front of is a decreasing functionof and is negative at. It is therefore negative forall. It follows that as a function of,is minimized at. It also follows from Eq.(33) that does notdepend on. To obtain a contradiction, it therefore suffices to show that
(35)
for and. Plugging into Eq.(32) gives
(36)
Using in the last equation, we get
(37)
which is a contradiction completing the proof.
Acknowledgements.
We thank Florent Balacheff, Maxime Fortier Bourque, Antonio Costa,Benson Farb, Svetlana Katok, Gabor Lippner, Marissa Loving, Curtis McMullen,Hugo Parlier, John Ratcliffe, and Chaitanya Tappu foruseful discussions and suggestions. This work was supported by NSF grantNos.IIS-1741355 andCCF-2311160.
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