**About These Exercises**

These exercises are meant entirely for your own practice, understanding, and potentially future research. I will not ask you to submit solutions. I strongly encouraged everyone to discuss these problems (and any related questions that you come up with) on the course discussion forum.

Problems that I’m sure are open are labeled **(!!)**.
Problems that I don’t know how to solve, but that I’m not confident are
open, are labeled *(?)*. Labeled problems may or may not be
difficult or interesting. Some exercises stray outside recent class
material, either into the future or outside the class entirely; feel
free to ask for clarification on Ed Discussion.

Some “open” problems on topics we haven’t covered yet may have been solved since the last time I taught this class. I’ll update relevant problems as the semester progresses.

Somewhat counterintuitively, the Jordan curve theorem does not extend
to *open* curves of the form \(\pi\colon \mathbb{R}\to\mathbb{R}^2\), even
when those curves are piecewise-linear. Let’s define an *infinite
polygonal chain* to be an open curve that passes through an infinite
sequence of points \(\dots, p_{-2}, p_{-2},
p_0, p_1, p_2, \dots\), where for each index \(i\), the subpath from \(p_{i-1}\) to \(p_i\) is the straight line segment \(p_{i-1}p_i\). As usual, an open curve is
*simple* if (as a function) it is injective.

Describe a simple infinite polygonal chain \(P\) such that \(\mathbb{R}^2\setminus P\) is connected. Give explicit coordinates for each vertex \(p_i\) as a function of its index \(i\).

Describe a simple infinite polygonal chain \(P\) such that \(\mathbb{R}^2\setminus P\) has two components. (This should be easy.)

Describe a simple infinite polygonal chain \(P\) such that \(\mathbb{R}^2\setminus P\) has three components.

Describe a simple infinite polygonal chain \(P\) such that \(\mathbb{R}^2\setminus P\) has four components.

(?) Prove that the complement \(\mathbb{R}^2\setminus P\) of any simple infinite polygonal chain has at most four components.

Call an infinite polygonal chain

*sane*if every vertex \(p_i\) has coordinates between \(-U\) and \(U\), and every edge \(p_ip_{i+1}\) has length between \(1\) and \(U\), for some real number \(U\gg 1\). (The precise value of \(U\) isn’t important; what*is*important is that \(U\) is finite.) Describe*sane*simple infinite polygonal chains \(P_1\), \(P_2\), and \(P_3\) such that for each index \(i\), the complement \(\mathbb{R}^2\setminus P_i\) has \(i\) components.(?) Prove that the complement \(\mathbb{R}^2\setminus P\) of any

*sane*simple infinite polygonal chain has at most three components.

In class we saw a proof, originally due to Dehn and Lennes, that the interior of any simple polygon in the plane can be frugally triangulated by adding interior diagonals. The proof relies on the Jordan curve theorem. In this problem we consider several extensions of the polygon triangulation theorem to spaces where the Jordan curve theorem does not hold.

Let \(P\) be a simple polygon that lies entirely in the interior of the unit square \(\square = [0,1]^2\). Prove that the area between \(\square\) and \(P\) can be triangulated. Every vertex of the triangulation must be a vertex of either \(P\) or \(\square\).

More generally, let \(P_0, P_1, P_2, \dots, P_k\) be pairwise-disjoint simple polygons such that the interior of \(P_0\) contains all the other polygons, but otherwise the interiors are disjoint. Prove that the area between \(P_0\) and the other polygons \(P_i\) (usually called a

*polygon with holes*) can be triangulated using only line segments between the vertices of the various polygons.A

*spherical polygon*is a circular sequence of points connected by great-circle arcs on the sphere. A spherical polygon is*simple*if it does not self-intersect. The Jordan curve theorem implies that any spherical polygon \(P\) divides the sphere into two components,*both*of which are bounded. Prove that it is possible to triangulate*both*of these components using great-circle arcs between vertices of \(P\).The

*infinite cylinder*is the product \(S^1 \times\mathbb{R}\) of the circle and a line. Prove that any geodesic polygon on the infinite cylinder can be triangulated.Prove that any geodesic polygon on the projective plane \(S^2 / \sim\) can be triangulated.

Prove that any geodesic polygon on the flat square torus \(S^1\times S^1\) can be triangulated.

For any simple polygons \(P\) and \(Q\), the Dehn-Schönflies theorem implies that there is a homeomorphism \(\phi \colon \mathbb{R}^2 \to \mathbb{R}^2\) such that \(\phi(P) = Q\). Moreover, if \(P\) has \(n\) vertices \(p_1,p_2,\dots,p_n\) and \(Q\) has \(n\) vertices \(q_1,q_2,\dots,q_n\), we can further require that \(\phi(p_i) = q_i\) for every index \(i\). This exercise asks you to construct such a homeomorphism explicitly.

Let \(\Box\) be a square that is
large enough to comfortably contain both \(P\) and \(Q\). We say that a triangulation \(T\) of \(\Box\) *supports* \(P\) if every vertex of \(P\) is a vertex of \(T\) and every edge of \(P\) is the union of edges of \(T\). (Unlike the previous problem, vertices
of \(T\) are not required to be
vertices of \(P\) or \(\Box\).) Two triangulations \(T_P\) and \(T_Q\) of \(\Box\) with labeled vertices are
*compatible* with \(P\) and
\(Q\) if they satisfy the following
conditions:

\(T_P\) and \(T_Q\) are isomorphic as labeled planar maps. That is, the vertex labeling induces bijections between the vertices, edges, and faces of \(T_P\) and the vertices, edges, and faces of \(T_Q\), respectively.

Corresponding vertices on the boundary of \(\Box\) have the same coordinates in both triangulations.

\(T_P\) supports \(P\), and \(T_Q\) supports \(Q\).

The vertex labeling also induces bijections between the vertices, edges, and interior faces of \(P\) and the vertices, edges, and interior faces of \(Q\), respectively. In particular, for any index \(i\), vertices \(p_i\) and \(q_i\) have the same label in \(T_P\) and \(T_Q\), respectively.

Describe an algorithm to compute compatible triangulations for two given \(n\)-gons with at most \(O(n^2)\) vertices. (This implies a piecewise-linear homeomorphism \(\phi\colon\mathbb{R}^2\to\mathbb{R}^2\) with complexity at most \(O(n^2)\) that is the identity outside the bounding box \(\Box\).)

Prove that the \(O(n^2)\) upper bound cannot be improved in the worst case.

*(?)*Describe an efficient algorithm to determine if two simple \(n\)-gons have compatible triangulations with exactly \(n+4\) vertices, namely, the vertices of the polygon plus the vertices of the bounding box \(\Box\).*(I’m reasonably confident that this problem can be solved in polynomial time.)***(!!)**Prove that computing compatible triangulations with the minimum number of vertices is NP-hard.**(!!)**Describe an algorithm that either computes triangulations of the flat square torus that are compatible with two given geodesic polygons \(P\) and \(Q\), or correctly reports that no such triangulation exists. (A compatible triangulation exists if and only if \(P\) and \(Q\) are either both contractible or both essential.) How many vertices are required in the worst case?

It may be easier to start by considering compatible triangulations
only of the interiors of the polygons, as described by Aronov, Seidel,
and Souvaine [*CGTA*
1993]. A similar problem for arbitrary point sets was previously
considered by Saalfeld [SOCG 1989]. Lubiw and
Mondal [CCCG
2017, *TCS* 2020]
recently proved that finding compatible triangulations for two polygons
*with holes* with a minimum number of vertices is NP-hard.

The lecture notes offer two different definitions of the winding number of a polygon \(P\) around a point \(o\):

- The sum of the signed angles at \(o\) subtended by the edges of \(P\)
- The number of positive crossings, minus the number of negative crossings, of edges of \(P\) by an arbitrary ray leaving \(o\).

Prove that these two definitions are in fact equivalent. *(Hint:
Triangulate \(P\).)*

Tired of the simple centuries-old game of Fast and Loose that everyone already knows, con artists Tenn and Peller are trying to develop more complex variants.

In their first variant, they plan to place the chain on the table so that it forms

*three*loops, and then invite the mark to put fingers into*two*of them. The mark wins if the chain is held fast to the table by their two fingers. Placing fingers in all three loops must hold the chain fast to the table. Describe how Tenn and Peller can always win. Equivalently, describe a closed curve \(C\) in the plane and three points \(p,q,r\) such that \(C\) is contractible in \(\mathbb{R}^2\setminus\{p,q\}\) and in \(\mathbb{R}^2\setminus\{p,r\}\) and in \(\mathbb{R}^2\setminus\{q,r\}\), but*not*contractible in \(\mathbb{R}^2\setminus\{p,q,r\}\). How long is the crossing sequence of your curve?In the harder variant, they place the chain so that it forms \(n\) loops, and then invite a crowd of marks to place fingers into any \(n-1\) of them. Equivalently, they want to want to design a curve that is non-contractible in the plane minus \(n\) points, but that becomes contractible if we ignore

*any one*of those \(n\) points. The crossing sequence of your curve should be a small polynomial in \(n\).

See “Picture-Hanging
Puzzles” by Demaine et al. [*TCS*
2013] for a different framing of this problem. For a more formal
treatment, see Gartside and Greenwood [*Fund. Math.* 2007].

Fix an arbitrary point \(o\) in the
plane, called the *origin*. Let \(P\) be a polygon in the *punctured*
plane \(\mathbb{R}^2\setminus \{o\}\)
with vertices \(p_0, p_1, \dots,
p_{n-1}\). A *vertex move* on \(P\) replaces an arbitrary vertex \(p_i\) with a new point \(q_i\). This vertex move is *safe* if
it preserves the widing number of the polygon around the original, that
is, neither of the triangles \(\triangle p_i
q_i p_{i-1}\) and \(\triangle p_i q_i
p_{i+1}\) contains \(o\). (All
index arithmetic is modulo \(n\).) A
sequence of safe vertex moves describes a homotopy between two polygons
with the same number of vertices and the same winding number around the
origin.

- Let \(n\) be an arbitrary odd integer. Let \(P\) be a regular star polygon with \(n\) vertices spaced evenly around the unit circle, with winding number \(\lfloor n/2 \rfloor\) around the origin, as shown below. Describe how to rotate \(P\) around the origin by half a circle using \(O(n)\) safe triangle moves.

Let \(P\) and \(Q\) be two

*arbitrary*polygons in \(\mathbb{R}^2\setminus \{o\}\) with the same number of vertices \(n\) and the same winding numer around the origin. Describe how to transform \(P\) into \(Q\) using \(O(n)\) safe vertex moves.*[Hint: Aim for a canonical polygon with the correct winding number; the star polygon in part (a) may not be the best candidate.]***(!!)**Now let \(O\) be a finite set of*obstacle*points in the plane, and let \(P\) and \(Q\) be*homotopic*polygons in \(\mathbb{R}^2\setminus O\) with the same number of vertices. How many safe triangle moves are necessary and sufficient to transform \(P\) into \(Q\) in the worst case? (Even the case \(k=2\) appears to be open.)**(!!)**Finally, let \(P\) and \(Q\) be two*arbitrary*polygons in \(\mathbb{R}^2\) with the same number of vertices \(n\) and the same*rotation*number. Now call a vertex move*dull*if it preserves the rotation number of the polygon; that is, as vertex \(p_i\) moves, the angle \(\angle p_{i-1} p_i p_{i+1}\) is never zero. (Mnemonically, the corner is never sharp.) Is it always possible to transform \(P\) into \(Q\) using \(O(n)\) dull vertex moves?

**(!!)**Describe an efficient algorithm to generate a random curve (up to isotopy) with a given number of vertices, drawn uniformly from the set of isotopy classes. Existing algorithms either require exponential time or don’t even approximate the uniform distribution well.- It is easy to generate Gauss codes uniformly at random, but most Gauss codes are non-planar.
- We can generate 4-regular plane graphs uniformly at random [Tutte 1964 and many others], but most such graphs are not unicursal.
- We can generate curves uniformly at random
*in the limit*using a Markov chain whose steps are homotopy moves [Chapman Rechnitzer 2018], but the convergence rate of this Markov chain is unknown.

**(!!)**In class we saw an algorithm of Steinitz to simplify an arbitrary generic planar curve with \(n\) vertices using \(O(n^2)\) homotopy moves. Moreover, Steinitz’s homotopy is*monotone*, meaning the number of vertices of the evolving curve never increases. Chang and Erickson described an algorithm that uses only \(O(n^{3/2})\) homotopy moves, but these can include \(0\to 2\) moves; they also proved a matching worst-case \(\Omega(n^{3/2})\) lower bound.Can every curve be

*monotonically*simplified using a sub-quadratic number of homotopy moves? Or is there a quadratic lower bound? (There*is*a quadratic lower bound if the homotopy is require to avoid even a single obstacle point.)**(!!)**In class we saw a efficient algorithm, morally due to Gauss, Nagy, and Dehn, to determine whether a given unsigned Gauss code describes a curve in the plane; Tarjan and Rosenstiehl improved the running time of this algorithm to \(O(n)\).Is there a polynomial-time algorithm to recognize unsigned Gauss codes of curves on more complex surfaces? Even the special case of the torus is open.

Exponential time is easy: Try all possible signings of the given Gauss code. Several characterizations are known, but all of them lead to exponential-time algorithms.

It may be easier to recognize Gauss codes of codes with special topological properties, such as null-homologous curves, contractible curves, curves in minimal position, or curves homotopic to a simple curve. As far as I know all these variants are open.

More ambitiously, how quickly can we determine the minimum-genus surface that supports a closed curve with a given unsigned Gauss code? Neither an efficient algorithm nor an NP-hardness result is known.

Let \(\Gamma = \{\gamma_1, \gamma_2, \dots,
\gamma_k\}\) be a set of generic closed curves that intersect
each other only pairwise, transversely, and away from their vertices; we
call any such set a *(generic) multicurve*. For simplicity,
assume that every curve \(\gamma_i\) is
either non-simple or intersects another curve \(\gamma_j\). Then the image of \(\Gamma\) is a 4-regular plane graph, which
may be disconnected. Conversely, every 4-regular plane graph is the
image of a family of planar curves.

A *Gauss paragraph* of \(\Gamma\) is a sequence of \(k\) strings, obtained by uniquely labeling
the vertices of \(\Gamma\), and then
listing the vertices in order along each curve \(\gamma_i\) in an arbitrary direction,
starting at an arbitrary basepoint, considering the curves in arbitrary
order.

Now let’s go the other direction. A Gauss paragraph is any set of non-empty strings in which any symbol appears exactly twice or not at all. Each Gauss paragraph \(X\) defines a 4-regular undirected graph \(G(X)\) whose vertices are the characters in \(X\) and whose edges correspond to adjacent character pairs. If \(X\) is the Gauss paragraph of a family of planar curves, then \(G(X)\) is the image graph of that family.

Prove that if \(X\) is the Gauss paragraph of a family of planar curves, then the edges of \(G(X)\) can be directed so that every vertex has in-degree \(2\) and out-degree \(2\). (Consider self-intersection points of one curve and intersection points of two curves separately.)

Describe a linear-time algorithm that either directs the edges of \(G(X)\) as described in part (a) or correctly reports that no such orientation exists, given the Gauss paragraph \(X\) as input.

Prove that \(X\) is the Gauss paragraph of a generic family of planar curves if and only if \(G(X)\) has a planar embedding such that every component has a non-self-crossing Euler tour.

Sketch a linear-time algorithm to decide whether a given sequence of strings is the Gauss paragraph of a generic family of planar curves. (Just describe the necessary modifications to the algorithm for single curves.)

Let \(P\) be an arbitrary polygon
with \(n\) vertices; for simplicity,
assume no three vertices in \(P\) are
collinear. The *image graph* \(G(P)\) is a planar straight-line graph
whose nodes are the vertices of \(P\)
and the intersection points of edges of \(P\). Let \(N\) denote the number of nodes in the image
graph \(G(P)\). Trivially, \(n\le N\le \binom{n}{2}\).

We can easily reduce \(P\) to a
triangle by repeated *vertex deletion*: replace some pair of
edges \(p_{i-1} p_i p_{i+1}\) with a
single edge \(p_{i-1}p_{i+1}\),
deleting the shared vertex \(p_i\).
Define the *cost* of deleting vertex \(p_i\) to be the number of nodes of \(G(P)\) that lie in the triangle \(\triangle p_{i-1}p_ip_{i+1}\),
*except* for the three vertices \(p_{i-1}\), \(p_i\), and \(p_{i+1}\). For example, if \(P\) is convex, every vertex deletion has
cost zero.

Prove that any vertex deletion with cost \(k\) can be transformed into a sequence of \(O(k)\) homotopy moves (\(1\leftrightarrow 0\), \(2\leftrightarrow 0\), or \(3\to 3\)), by treating the polygon as a generic curve. (Watch out for spurs!) This is the motivation for my definition of cost.

Trivially, every vertex deletion has cost \(O(n^2)\), and therefore any sequence of \(n-3\) vertex deletions has total cost \(O(n^3)\). Prove that this \(O(n^3)\) bound is tight in the worst case. (See problem 1.5!)

Prove that if \(P\) is simple, there is a sequence of \(n-3\) vertex deletions with total cost \(0\).

Prove that any polygon \(P\) can be reduced to a triangle by a sequence of \(n-3\) vertex deletions with cost \(O(N^2)\). (A vertex deletion can actually increase \(N\).)

**(!!)**Prove or disprove: There is a sequence of \(n-3\) vertex deletions with total cost \(O(N^{3/2})\). Your proof of part (b) implies a worst-case \(\Omega(N^{3/2})\) lower bound.

**(!!)** Let \(s, p_1, p_2,
\dots, p_n\) be disjoint points in the plane. A **proper
squid** is a collection of paths \(\pi_1, \pi_2, \dots, \pi_n\), where each
path \(\pi_i\) connects \(s\) with the corresponding point \(p_i\), such that (1) the paths are simple
and disjoint (except at \(s\)), and (2)
the paths are incident to \(s\) in
counterclockwise order \(\pi_1, \pi_2, \dots,
\pi_n\).

A **squid** is a collection of paths from \(s\) to the points \(p_i\) that can be perturbed by an
arbitrarily small distance into a proper squid.

Either describe an algorithm for the following problem, or prove that it is NP-hard (or worse): Given points \(s, p_1, p_2, \dots, p_n\), find the minimum-length squid connecting those points. The minimum-length squid consists entirely of polygonal paths through the obstacle points \(p_j\).

I know how to solve this problem in exponential time. We can compute
the minimum-length squid in a given *homotopy* class in
polynomial time using the funnel algorithm; the hard part of the problem
is finding the right homotopy class.

The problem is open even when all \(n+1\) points lie on a line.

*Polyhedra* are natural three-dimensional generalization of
polygons, but they are surprisingly difficult to define precisely. In
particular, formally defining *regular* polyhedra is remarkably
subtle. It has been common knowledge for *millennia* that the
five Platonic solids — tetrahedron, cube, octahedron, dodecahedron, and
icosahedron — are the only regular polyhedra; we can find a proof in
Euclid’s *Elements* (Book XIII, Proposition 18). But is that
actually correct?

Here is one attempt at a reasonable definition. Let \(\Sigma = (V, E, F)\) be a **finite
planar** map with vertices \(V\), edges \(E\), and faces \(F\). A *polyhedral realization* of
\(\Sigma\) is a function assigning
coordinates to vertices so that (1) the endpoints of each edge map to
distinct points, and (2) the vertices of each face (including the outer
face) map to coplanar points. Informally, \(\pi\) maps each edge of \(\Sigma\) to a line segment and each face of
\(\Sigma\) to a polygon in some plane
in \(\mathbb{R}^3\). A
*polyhedron* is a polyhedral realization of a finite planar map.
We call the images of \(V\), \(E\), and \(F\) the vertices, edges, and faces of the
polyhedron.

Just like polygons, polyhedra are not required *by definition*
to be simple. In particular, polyhedra can have overlapping features,
including coincident vertices, collinear intersecting edges, and
coplanar intersecting faces. However, for purposes of this exercise, I’m
requiring polyhedra to be images of *finite planar* maps.

A *flag* in a planar map or a polyhedron is any triple \((v,e,f)\), where \(v\) is an endpoint of edge \(e\) and \(e\) is an edge of face \(f\). A planar map is *regular* if it
has a flag-transitive automorphism group. That is, for any two flags
\((v,e,f)\) and \((v',e',f')\), there is an
bijection \(\alpha\colon\Sigma\to\Sigma\) such that
\(\alpha(v) = v'\) and \(\alpha(e)=e'\) and \(\alpha(f) = f'\). Finally, a polyhedron
is regular if, for any two flags, there is a rotation or reflection of
\(\mathbb{R}^3\) that maps one flag to
the other. In particular, the underlying planar map of any regular
polyhedron is regular.

Give a complete list of all finite regular planar maps.

Give a complete list of all regular polyhedra. (Hint: There are more than five.)

**(!!)**Suppose we remove the word “planar” from the definition of polyhedron. Give a complete list of all regular polyhedra under this more general definition.**(!!!)**Suppose we remove the word “finite” from the definition of polyhedron (but we keep “planar”). Give a complete list of all regular polyhedra under this more general definition.**(!!!!)**Suppose we remove both “finite” and “planar” from the definition of polyhedron. Give a complete list of all regular polyhedra under this more general definition.

Prove the following facts about planar graphs and maps with \(n\) vertices.

- Every
*simple*planar graph has a vertex of degree at most 5. - Every
*simple*planar graph has at most \(3n - 6\) edges. - Every simple planar
*bipartite*graph has at most \(2n - 4\) edges. - Every planar map has either a vertex of degree at most 3 or a face of degree at most 3.
- Every planar graph has an independent set of size \(\Omega(n)\) in which every vertex has degree \(O(1)\).

So far we’ve seen two simple representations of closed curves in the plane: polygons (specified by vertex coordinates) and generic curves (specified by signed Gauss codes or rotation systems). Another natural representation is as closed walks in embedded graphs.

Let \(\Sigma\) be a fixed
combinatorial planar map, called the *scaffold*, represented by a
rotation system and a choice of outer face. Recall that a closed walk in
\(\Sigma\) is a circular sequence of
darts \(W = (d_0, d_1, \dots, d_\ell)\)
such that \(\textsf{head}(d_i) =
\textsf{tail}(d_{(i+1)\bmod \ell})\) for every index \(i\).

Describe a fast algorithm to compute an

*Alexander numbering*for a given closed walk \(W\) in \(\Sigma\). For each face \(f\) of \(\Sigma\), your algorithm should compute the winding number of \(W\) around and point in \(f\). (Why are winding numbers independent of the geometry of \(\Sigma\)?)A

*spur*in a closed walk is an adjacent pair of darts \(d_i,d_{i+1}\) such that \(d_{i+1} = \textsf{rev}(d_i)\). Describe an algorithm to compute the*rotation number*of a spur-free closed walk in \(\Sigma\). (Why is the rotation number independent of the geometry of \(\Sigma\)?)Suppose several bounded faces \(f_1, \dots, f_h\) of \(\Sigma\) are specified as

*holes*. Describe an algorithm to decide whether two closed walks \(W\) and \(W’\) in \(\Sigma\) are homotopic in \(\mathbb{R}^2 \setminus (f_1\cup \cdots \cup f_h)\).

None of your algorithms should compute an actual geometric embedding of \(\Sigma\); instead, they should work directly with the rotation system.

When we think about algorithms for planar graphs, it is often useful to make assumptions about the degrees of vertices and/or the degrees of faces. Let \(\Sigma\) be a simple undirected planar map with weighted edges.

We can assume without loss of generality that every

*face*of \(\Sigma\) has degree \(3\), by inserting diagonals into any higher-degree face. Moreover, we can preserve shortest-path distances by giving these new edges infinite weight (length), and we can preserve maximum flow values by giving the new edges weight (capacity) zero.On the other hand, we can assume without loss of generality that every

*vertex*of \(\Sigma\) has degree 3, by expanding each higher-degree vertex into a tree of degree-3 vertices (or equivalently, by triangulating faces in the dual graph \(\Sigma^*\)). Moreover, we can preserve shortest-path distances by giving these new edges weight (length) zero, and we can preserve maximum flow values by giving the new edges infinite weight (capacity).

Unfortunately, triangulating faces increases vertex degrees, and
expanding vertices increases face degrees; these two assumptions seem to
cancel each other out. What if we need *both* vertices
*and* faces to have small degree?

Prove that for some constants \(\Delta\), \(\Delta^*\), and \(c\), any simple \(n\)-vertex planar graph \(\Sigma\) can be modified by inserting and expanding edges into a new simple planar map \(\tilde\Sigma\) with at most \(cn\) vertices, such that each vertex of \(\tilde\Sigma\) has degree at most \(\Delta\) and each face of \(\tilde\Sigma\) has degree at most \(\Delta^*\). Try to keep the product \(\Delta\cdot\Delta^*\cdot c\) as small as possible.

More simply: **Argue that without loss of generality, planar
maps have both bounded vertex degrees and bounded face
degrees.**

The duality between planar maps extends to embeddings of
*directed* planar graphs as well. A directed graph still has two
darts for every edge, but now one dart is marked as “really there”; the
other is used for navigation, but nothing else. Duality extends to
directed graphs by following the darts; the dual of any directed edge
\(u\mathord\to v\) is the directed edge
\(\mathsf{right}(u\mathord\to
v)^*\mathord\to\mathsf{left}(u\mathord\to v)^*\).

We call a vertex \(v\) in a directed
planar map *regular* if its cycle of incident edges consists of a
single interval of incoming edges followed by a single interval of
outgoing edges. We call \(v\) a
*saddle* if \(v\) is incident to
four edges that are directed into \(v\), out of \(v\), into \(v\), and out of \(v\) in cyclic order around \(v\).^{1}

Prove that a planar graph \(G\) is strongly connected if and only if its dual graph \(G^*\) (with respect to

*any*planar embedding) is acyclic.Let \(G\) be a directed acyclic planar graph with a unique source \(s\) and a unique sink \(t\). Prove that in

*every*planar embedding of \(G\), every vertex except \(s\) and \(t\) is regular.Let \(G\) be a (weakly) connected directed acyclic planar graph with \(k\) sources and \(\ell\) sinks. Prove that every planar embedding of \(G\) contains at most \(k+\ell-2\) saddle vertices.

Our proof of the Tutte spring-embedding theorem assumed that every
stress coefficient \(\lambda_{u\mathord\to
v}\) is positive. But in fact, the theorem extends fairly easily
to the setting where some stress coefficients are zero. Zero-weight
darts allow us to model *directed* planar graphs, by identifying
directed edges with positive-weight darts. (We still keep the
zero-weight darts in our graph data structure for navigational
purposes.) A drawing/embedding of a directed graph is a
drawing/embedding of its underlying undirected graph; each directed edge
corresponds to an orientation of the corresponding undirected edge
path.

Find the weakest condition you can for planar graphs with non-negative dart coefficients that implies that the Tutte drawing is a strictly convex embedding. For example, “simple 3-connected planar graph with all positive stress coefficients” is sufficient, but stronger than necessary.

As a starting point, consider planar graphs in which every stress
coefficient is either \(0\) or \(1\). Equivalently, consider Tutte drawings
of directed planar graphs, where every interior vertex is positioned at
the center of mass of its *in*-neighbors.

The *transshipment* problem is the uncapacitated special case
of the minimum-cost flow problem. A transshipment network consists of a
directed graph \(G\), a
*balance* \(b(v)\) for each
vertex \(v\), and a *cost* \(\$(e)\) for each directed edge \(e\). The vertex balances must sum to zero;
positive balances are interpreted as demand, and negative balances as
supply.

A feasible flow in a transshipment network \((G, b, \$)\) assigns a non-negative real value \(f(e)\) to each directed edge \(e\), such that \(\sum_u f(u\mathord\to v) - \sum_w f(v\mathord\to w) = b(v)\) at every vertex \(v\); that is, the total net flow into \(v\) must equal the balance of \(v\). The goal of the transshipment problem is to compute a feasible flow \(f\) with minimum total cost \(\$(f) = \sum_e \$(e)\cdot f(e)\).

Every transshipment network has a minimum-cost flow that is supported on a spanning tree \(T\), meaning \(f(e)\) for every non-tree edge \(e\). (More generally, every basic feasible solution to the transshipment linear program is a feasible flow supported on a spanning tree.) We can always symbolically perturb the edge costs so that the minimum-cost flow is unique.

The single-source shortest-path problem is the special case of transshipment where the source vertex \(s\) has balance \(1-n\), every other vertex has balance \(1\), and the cost of an edge is its length. The transshipment tree \(T\) is just the shortest path tree rooted at \(s\); if \(u\) is the parent of \(v\) in \(T\), then \(f(u\mathord\to v)\) is equal to the number of descendants of \(v\) (including \(v\) itself).

Now consider the following alternative strategy for the multiple-source shortest path problem. Let \(s_1, s_2, \dots, s_k\) be the vertices on the outer face of the input planar map \(\Sigma\). For each index \(i\), let \(T_i\) denote the transshipment tree for this network when \(b(s_i) = 1-n\) and \(b(v)=1\) for all \(v\ne s_i\). We begin by computing \(T_1\) from scratch (using Dijkstra’s algorithm, for example). Then in the \(i\)th phase, we transform \(T_i\) into \(T_{i+1}\) by linearly interpolating the balances and maintaining a transhipment tree. That is, we maintain the transshipment tree \(T_\lambda\) for the balances \[ b_\lambda(v) = \begin{cases} 1 - n + n\lambda & \text{if $v = s_i$} \\ 1 - n\lambda & \text{if $v = s_{i+1}$} \\ 1 & \text{otherwise} \end{cases} \] as the parameter \(\lambda\) continuously increases from \(0\) to \(1\). Just as in the parametric-shortest-path formulation, the transshipment tree changes at discrete times by pivots: An edge pivots out when its flow value drops to zero, and the minimum-cost edge that reconnects the tree pivots in.

Prove that over the entire algorithm, each directed edge pivots into
the transshipment tree at most once and out of the transshipment tree at
most once. (Hint: Argue that the sequence of pivots is *almost*
identical to the sequence of parametric shortest-path tree pivots!)

Every vertex in the Nagy graph of a generic plane curve is a saddle.↩︎