# A fixed parameter algorithm for optimal convex partitions

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## A ﬁxed parameter algorithm for optimal convex partitions

### Andreas Spillner

University of East Anglia, Norwich, United Kingdom

a r t i c l e i n f o a b s t r a c t MSC: 68W40 Keywords: Convex partition Fixed-parameter algorithm

We present a ﬁxed-parameter algorithm for the Minimum Convex Partition and the Minimum Weight Convex Partition problem. The algorithm is based on techniques developed for the Minimum Weight Triangulation problem. On a set P of n points the algorithm runs in O(2kk4n3+n log n)time. The parameter k is the number of points in P lying in the interior of the convex hull of P .

1. Introduction

Many two-dimensional geometric optimization problems turn out to be NP-hard or at least no polynomial time exact algorithm to solve them is known. A prominent example is the Traveling Salesman problem. One quite successful way of dealing with this situation is to aim at approximate solutions only . Another possible approach is to develop exact algorithms within the framework of ﬁxed-parameter complexity theory. It seems that the potential of the latter option remains widely unexplored for geometric problems. Some rare exceptions include certain problems whose input consists essentially of a set P of n points in the plane. They have been successfully parameterized with the number k of points in P lying in the interior of the convex hull of P [3,8,10]. The motivation to employ this particular parameter was the observation that these optimization problems can be solved in polynomial time if all input points appear on the boundary of the convex hull of P , i.e., if k

### =

0. In this sense, k can be understood as measuring the “distance from triviality”.

In this paper we want to show that the techniques developed for the Minimum Weight Triangulation problem and presented incan be extended to the Minimum Convex Partition problem and the Minimum Weight Convex Partition problem as outlined in.

In the Minimum Weight Triangulation problem (MWT) we want to compute a triangulation of the point set P such that the total edge length is minimum. This problem was recently shown to be NP-hard by Mulzer and Rote . Hoffmann and Okamotogive a ﬁxed-parameter algorithm for MWT running in O

6kn5log n

### )

time. Init was shown that the approach of Hoffmann and Okamoto can be improved to yield an algorithm running in O

2kkn3

n3

### )

time. Independently Grantson et al. presented algorithms for MWT running in O

4kkn4

and O

k

kn3

### )

time, respectively.

Convex partitions generalize triangulations in that it is no longer required that the regions induced by the edges in the partition are triangles. It suﬃces if the regions are convex. So every triangulation is a convex partition but not vice versa.

The Minimum Convex Partition problem (MCP) is to compute a convex partition of P such that the number of convex regions is minimum. Lingas has shown that the related problem of partitioning a polygon with n vertices by diagonals into a minimum number of convex pieces is NP-hard for polygons with holes. For polygons without holes Keil and Snoeyink give an O

n3

### )

time algorithm . Fevens et al. have shown that MCP can be solved in O

n3h+3

### )

time if the points in

The results of this paper have been presented at the 17th Canadian Conference on Computational Geometry [A. Spillner, Optimal convex partitions of

point sets with few inner points, in: Proc. Canadian Conference on Computational Geometry, 2005, pp. 34–37]. E-mail address:aspillner@cmp.uea.ac.uk.

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(a) (b) (c)

Fig. 1. A point set, a general partition and a convex partition of this point set.

P lie on h nested convex hulls . Grantson and Levcopoulos present a ﬁxed-parameter algorithm for MCP running in

O

k6k−5216kn

### )

time.

The Minimum Weight Convex Partition problem (MWCP) is to compute a convex partition E of P such that the total length of the edges in E is minimum. Again we have the related problem of partitioning a polygon with n vertices into convex pieces such that the total length of the diagonals used for the partition is minimum. This related problem is NP-hard for polygons with holes, as shown by Keil . But it can be solved in O

n4

### )

time for polygons without holes as Keil and Snoeyink note in. There are also polynomial time constant-factor approximation algorithms for MWCP by Plaisted and Hongand Levcopoulos and Krznaric. The O

n3h+3

### )

time algorithm of Fevens et al.developed for MCP also applies to MWCP. Borgelt et al. give a ﬁxed-parameter algorithm running in O

k4k−8213kn3

### )

time.

We will present a ﬁxed-parameter algorithm for MCP and MWCP running in O

2kk4n3

n log n

### )

time. The paper is structured as follows. In Section 2 we state the problems under consideration more formally and brieﬂy introduce the concept of ﬁxed-parameter algorithms[4,16]. In Section3we present our algorithm and we conclude in Section4.

2. Preliminaries

Let P denote a set of n points in the plane. By CH

P

### )

we denote the convex hull of P . We call the vertices of CH

P

### )

outer points for short. We denote the set of outer points in P by Pout and the set of edges of CH

P

### )

by Eout. Analogously, we call the points of P that lie in the interior of CH

P

### )

inner points and deﬁne Pin

P

### \

Pout. An example of a set P of points is given in Fig. 1(a) where the points in Pout and Pin are drawn as solid disks and empty circles, respectively. To ease argumentation we will make the simplifying assumptions that no three points in P are collinear and no two points in P have the same x-coordinate. Note that the algorithm to be presented can easily be adapted to work without these assumptions.

We will refer to the relative location of points in the plane in the following way. We will say that a point q is between points p and r, written as p

q

### ≺

r, if q has larger x-coordinate than p and smaller x-coordinate than r. In the same

situation we will also say that p is to the left of q, written as p

### ≺

q, and r is to the right of q.

A partition of P is a set E of straight line segments with endpoints in P , called edges, such that edges do not cross each other. Two edges cross if they share a point which is not an endpoint of both edges. An edge with endpoints p and q we denote by pq. We will consider only partitions E of P that contain all the edges forming the boundary of CH

P

, that is

Eout

### ⊆

E. The edges in such an E partition the interior of CH

P

into a ﬁnite set

E

### )

of regions. A region is a connected component of the set of those points of the interior of CH

P

### )

that do not belong to any edge in E. Note that the regions in

E

### )

are open subsets of

### R

2. For a region R

E

let B

R

### )

denote the set of those edges in E that are contained in the boundary of R. InFig. 1(b) we show the edges of a partition E of the point set inFig. 1(a). The edges in E partition the interior of the convex hull into the set

E

R1

R2

R3

### }

of regions.

A partition E of P is called a convex partition of P if every region in

E

### )

is convex and does not contain a point of P . An example of a convex partition is given in Fig. 1(c). A convex partition E of P is a minimum convex partition of P if the number of regions in

E

### )

is minimum. A convex partition E of P is a minimum weight convex partition of P if the total length of the edges in E is minimum.

A problem is ﬁxed-parameter tractable if it can be solved by an algorithm running in O

f

k

p

n

### ))

time. Such an al-gorithm is called a ﬁxed-parameter alal-gorithm. The function f

k

### )

is allowed to depend only on the so-called parameter k. The parameter k should be a quantity associated with the input such that k can be small even for large input size n. The function p

n

### )

must be a polynomial of constant degree. As mentioned before we will use the number of inner points in P as the parameter, that is, k

Pin

### |

. The size of the input is the number of points in P .

3. The algorithm

Since our algorithm does not exploit any geometric properties that depend on the objective function, it will work for both problems under consideration. We will concentrate on the problem to minimize the number of regions in a convex partition, i.e., on MCP. The problem MWCP can be treated completely analogously.

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(a) (b) (c)

Fig. 2. Regions that can be described by a monotone path.

3.1. The basic idea

Our algorithm relies on a further generalization of the idea of Hoffmann and Okamoto  to split a problem into independent subproblems by monotone paths. A monotone path is a sequence

a1a2

a2a3

al−1al

### }

of edges in a partition E of P with the property that ai

ai+1 for every i

1

l

1

### }

.

Observation 1. Let E be a convex partition and

a1a2

al−1al

### }

a monotone path contained in E. The situation is shown in Fig. 1(c) with l

4. If al

### ∈

Pinthen there exists at least one z

P such that al

z and alz

### ∈

E. Hence, we can extend the monotone

path by an edge in E beyond alto the right. Similarly, we can extend the monotone path beyond a1to the left if a1

### ∈

Pin.

The idea is to search for a monotone path

### π

that is contained in a minimum convex partition of P with the property that both endpoints of

### π

are outer points and no other point on

### π

is an outer point. Then the partition Eout

### π

induces two regions R1 and R2 as shown in Fig. 2(a). Note that we can then deal with each of R1 and R2 independently, since a convex partition E of P that contains

### π

has a minimum number of regions if and only if both

R

E

: R1

R

and

R

E

: R2

R

### = ∅}|

are minimized. In this sense the regions R1 and R2 can be viewed as subproblems our algorithm must solve. The description of these subproblems is very simple, essentially we just need to specify the monotone path

### π

. We would like to maintain this simplicity of description when we deal with subproblems of subproblems and so on, since this is essential to obtain a good bound on the running time of our algorithm. In the remainder of this section we present the key ideas and observations that help to achieve this goal. We start with another observation.

Observation 2. Let E be a convex partition of P that contains an edge e which is not an edge of CH

P

### )

but both endpoints of e are outer points. The situation is shown inFig. 1(c). Then E

e

### }

is also a convex partition of P . Thus, a minimum convex partition of P will never contain such an edge e.

To continue we ﬁrst need to introduce some more notation. For u

v

Pout, u

v, let Pout

u

v

### )

denote the set of those outer points we meet when we walk in clockwise direction along the boundary of CH

P

### )

from u to v. Similarly, let Eout

u

v

### )

denote the set of edges in Eout that we meet on this walk. Since we view regions in a partition of P as subproblems, we want to be able to talk about optimal solutions for these subproblems: Let E be a partition of P and R

E

### )

. Let E be a (convex) partition of P with E

### ⊆

E . Then we call the set of edges E

### |

R consisting of those edges in E that are contained in the closure of R a (convex) partition of R and deﬁne

E

R

R

E

: R

R

### = ∅}

. If E is a convex partition such that

E

### |

is minimized then we call E

### |

R a minimum convex partition of R. Note that B

R

### )

is a subset of every partition of R. For a set of points Q we denote the interior of the convex hull of Q by CHo

Q

### )

.

Now consider a region R in a partition of P such that B

R

Eout

u

v

### π

for some monotone path

a0a1

a1a2

alal+1

with endpoints u

a0 and v

### =

al+1. The endpoints are the only points on

### π

that are outer points and we assume without loss of generality that u

### ≺

v. An example is given inFig. 2(b). Now consider any edge e

pq

, p

q,

and let E

### |

R be a convex partition of R. Then there is a unique convex region R

E

R

with e

B

R

### )

. We say that R is spanned by edge e if for every vertex z of R , z

p

q

### }

, it holds that p

z

### ≺

q. Note that if R is spanned by e then each of the regions in

B

R

B

R

R

### }

of the partition B

R

B

R

### )

of R can be described by a monotone path, too, as shown in Fig. 2(c) where those regions are indicated by shading. Hence, it would be nice if there was always a minimum convex partition E

R of R such that

E

R

### )

contains a region that is spanned by some edge on

### π

. However, it is easy to come up with examples where this is not the case. To get around this problem, we study the structure of convex partitions of R a little closer. The results relevant for our algorithm are summarized in the following lemma.

Lemma 1. Let R be a region as described above and let E

### |

R denote a convex partition of R. Let K denote the set of those edges in

E

R

B

R

### )

that have at least one endpoint in

a1

al

### }

. If K is non-empty then at least one of the following is true:

(1) There is an i

1

l

### }

such that there is no edge in K incident to aj, 1

j

### <

i, and there is an edge aiz

K with z

### ≺

ai (intuitively: points a0

### , . . . ,

aiare vertices of a convex region in E

### |

Rand aiis the rightmost vertex of this region).

(2) There are i1

i2

1

l

, i1

### <

i2, such that there is no edge in K incident to aj, i1

j

### <

i2, and there are edges ai1z1

ai2z2

K with ai1

z1and z2

### ≺

ai2(intuitively: points ai1

### , . . . ,

ai2are vertices of a convex region in E

### |

R, and ai1and ai2 are the leftmost and the rightmost vertex of this region, respectively).

(4)

(a) (b) (c)

Fig. 3. Examples of regions R with shapes (i), (ii) and (iii).

(a) (b) (c)

Fig. 4. Examples of regions R with shape (iv).

(3) There is an i

1

l

### }

such that there is no edge in K incident to aj, i

j

### 

l, and there is an edge aiz

K with ai

### ≺

z (intuitively: points ai

### , . . . ,

al+1are vertices of a convex region in E

### |

Rand aiis the leftmost vertex of this region).

Proof. Let h1 denote the smallest index in

1

l

### }

such that there is an edge ah1z1 in K . If z1

### ≺

ah1 then (1) holds.

Otherwise we have ah1

### ≺

z1. If no edge of K is incident to aj, h1

j

### 

l, then (3) holds. Otherwise let h2 denote the

smallest index in

h1

1

l

### }

such that there is an edge ah2z2 in K . If z2

### ≺

ah2 then (2) holds. Otherwise we iterate the

argument with h2 playing the part of h1, that is, we look for the smallest index h3

h2

1

l

### }

such that there is an edge ah3z3 in K and so on. Since K is a ﬁnite set we will eventually arrive in a situation where (2) or (3) holds.

### 2

Our algorithm computes a minimum convex partition E

### |

R for a region R deﬁned by a monotone path

### π

by checking the possible shapes of a convex region R in

E

R

### )

that has an edge of

### π

on its boundary. This will lead to new types of subproblems and we will formally describe them in the next section. In the remainder of this section we want to present the relevant shapes of region R and how we can, for each of these shapes, decompose R along at most three additional edges into convex regions. This will help to keep the number of types of subproblems small since it will be suﬃcient to check for the structure of these additional edges and not for all the (possibly very many) edges in B

R

### )

.

The shapes (i), (ii) and (iii) of R below arise essentially in situations (1), (2) and (3), respectively, described inLemma 1. Note, however, that further requirements must be met for each of these shapes and we need to make sure that the situation where the set K is empty is also treated properly.

Shape (i). Region R has this shape if there exists i

1

l

1

such that

a0

a1

ai

### }

is a subset of the vertices of R , all the remaining vertices of R are to the left of aiand at least one vertex of R is to the left of a0. An example of a region

R that has this shape is given inFig. 3(a). Note that some of the vertices of R that do not lie on

### π

can be outer points. Next we describe how R can be decomposed. Let z denote the leftmost vertex of R and consider the two edges e1

a0z and e2

### =

aiz. If edge ej, j

1

2

### }

, is not an edge in B

R

### )

then ejcuts off a convex subregion of R which is spanned by ej. In the example inFig. 3(a) the additional edges e1and e2decomposing R are drawn dotted.

Shape (ii). Region R has this shape if there exist i1

i2

0

1

l

1

, i1

i2, such that

ai1

ai2

### }

is a subset of the

vertices of R and all the remaining vertices of R are between ai1 and ai2. An example of a region R that has this shape is

given inFig. 3(b).

If i2

i1

### +

1 then edge ai1ai2 spans region R . Otherwise we use e

### =

ai1ai2 as an additional edge that decomposes R

into two convex subregions each spanned by e. In the example inFig. 3(b) the additional edge e is drawn dotted.

Shape (iii). Region R has this shape if here exists i

0

l

such that

ai

al+1

### }

is a subset of the vertices of R , all the remaining vertices of R are to the right of ai and at least one vertex of R is to the right of al+1. This shape is symmetric to Shape (i). An example of a region R that has this shape is given inFig. 3(c).

Shape (iv). Region R has this shape if

a0

a1

al+1

### }

is a subset of the vertices of R , at least one vertex of R is to the left of a0 and at least one vertex of R is to the right of al+1. An example of a region R that has this shape is given in Fig. 4(a).

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(a) (b) (c) (d)

Fig. 5. Examples of subproblems of Type 1, 2, 3 and 4.

The additional edges we use for decomposing R depend on the structure of R . If l

### 

1 then we use the single edge

e

### =

uv. If edge e is not an edge in B

R

### )

then e partitions R into two convex subregions. An example is given inFig. 4(a). If l

0 then

### π

consists of the single edge uv. If R has a vertex w

Pout

u

v

u

v

### }

then we use the two edges e1

u w and e2

### =

w v to decompose R . An example is given inFig. 4(b). It remains to consider the situation that u and v are the only outer points on the boundary of R . Let z1 denote the leftmost vertex of R and z2the rightmost vertex of R . Then we use the three edges e1

uz1, e2

z1z2 and e3

### =

v z2to decompose R . If edge ej, j

1

2

3

### }

, is not an edge of R then ej cuts off a convex subregion of R which is spanned by ej. An example is given inFig. 4(c).

This ﬁnishes the description of the relevant shapes of R .

3.2. Description of subproblems

In this section we give a formal description of those types of regions that our algorithm might encounter. The deﬁni-tion of these types is motivated by the observadeﬁni-tions made in Secdeﬁni-tion 3.1. A subproblem R will be deﬁned by a 4-tuple

t

u

v

A

M

.

t

1

2

3

4

### }

indicates the number of the type of the subproblem.

### •

u and v are two distinct outer points where u

v. The set Eout

u

v

is a subset of B

R

.

A

a1

al

### }

is a subset of Pin with

A

l and a1

a2

al. Let

A

a1a2

al−1al

### }

denote the monotone path through the points in A. It will be convenient to refer to u and v as a0 and al+1, respectively.

### •

M is a subset of B

R

### )

. The edges in M are supposed to span a region of the convex partition of R we want to compute. The main purpose of the set M is to mark edges in B

R

### )

that originate from the decomposition of those shapes of regions as described at the end of Section3.1.

Type 1. We have u

a1and al

v. If A

then B

R

Eout

u

v

A

ua1

alv

. Otherwise B

R

Eout

u

v

uv

.

M

1 and M

B

R

Eout

u

v

### )

. An example of this type is given inFig. 5(a).

Type 2. We have

A

1, u

a1 and al

v. There are i

j

0

l

1

, i

j, j

i

2, such that B

R

Eout

u

v

A

ua1

alv

aiaj

aj−1aj

and M

aiaj

, CHo

ai

aj

R and CHo

ai

aj

P

### = ∅

. An example of this type is given inFig. 5(b).

Type 3. We have

A

### | 

1, point a1 lies in CHo

Pout

u

v

, a1

u and al

### ≺

v. Let aj denote the rightmost point in A which is to the left of u. We have B

R

Eout

u

v

A

uaj

alv

and M

uaj

ajaj+1

### }

. An example of this type is given in Fig. 5(c).

Type 4. All the points in A lie in CHo

Pout

u

v

. We have

A

2, a1

u and v

a2, B

R

Eout

u

v

A

ua1

va2

, and M

ua1

a1a2

va2

### }

. An example of this type is given inFig. 5(d).

This ﬁnishes the formal description of the types of subproblems. In the next section we will outline how to compute solutions for subproblems. There it will be convenient to say that a region has a certain type if we can make the region conform with the description of the type given above by a reﬂection on the x-/ y-axis or a rotation by 180◦.

3.3. Processing of subproblems

Let E

### |



R denote a ﬁxed minimum convex partition of R. Let

R

### )

denote the number of regions in

E

R

### )

. In the following we describe how we compute for each subproblem R described by a 4-tuple

t

u

v

A

M

the value

t

u

v

A

M

R

### )

. We adopt the convention that min

### ∅ = +∞

. To avoid the explicit treatment of degenerate sit-uations we extend the collection of subproblems of Type 1 to 4-tuples

1

u

v

where Pout

u

v

u

v

### }

. For such degenerate subproblems of Type 1 we set

1

u

v

### ∅, ∅) =

1. Note that the number of regions of a minimum convex partition of P equals

1

u

v

### ∅, ∅)

where u and v are any two outer points with the property that Pout

u

v

### =

Pout. In the following for each type of subproblem we use the notation introduced in Section3.2.

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1

min

1

u

w

1

w

v

1

subject to w

Pout

u

v

u

v

and CHo

u

v

w

P

### = ∅

. Note that we rely onObservation 2: neither edge u w nor edge

w v are contained in an optimal solution.

Next we consider the situation where region R has Shape (i). We compute

2

min

3

u

v

z

M

M

1

subject to z

Pin

R, z

u, M

uz

v z

and CHo

u

v

z

P

### = ∅

. Here our strategy is to ﬁrst ﬁx the leftmost vertex of region R . We check every possible candidate z for this vertex. Once z is ﬁxed this also ﬁxes the edges uz and v z that decompose R (the dotted edges inFig. 3(a)). Then we use the set M to mark which of them is contained in the boundary of R . Note that in the following we will use analogous strategies repeatedly without giving the details again.

Next we consider the situation where region R has Shape (ii). Then we just mark edge uv as spanning R , that is, we compute

3

1

u

v

uv

### .

Next we consider the situation where region R has Shape (iii). Since this is symmetric to shape (i) we compute

4

min

3

u

v

z

M

M

1

subject to z

Pin

R, v

z, M

uz

v z

and CHo

u

v

z

P

### = ∅

. Note that we need not consider the situation where

z is an outer point for shapes (i) and (iii), as we have dealt with that already in the computation of

### ϕ

1.

Finally, we consider the situation where R has Shape (iv) and u and v are the only outer points on the boundary of R . We compute

5

min

4

u

v

z1

z2

M

M

1

subject to z1

z2

Pin

R, z1

u, v

z2, M

uz1

z1z2

v z2

and CHo

u

v

z1

z2

P

. This yields

R

min

1

5

.

Case 2: A

and M

### = ∅

. First we consider the situation that there is a region R in

E



R

### )

of Shape (i). We distinguish whether the leftmost vertex of R is an outer or an inner point. So, ﬁrst we compute

1

min

1

u

w

1

w

v

A

a1

ai−1

M

M

subject to 1

i

l, w

Pout

u

v

u

v

, w

u, CHo

a0

ai

w

P

, CHo

a0

ai

w

R and M

aiw

### }

. Then we compute

2

min

3

u

v

A

a1

ai−1

z

M

M

1

subject to 1

i

l, z

Pin

R, z

u, CHo

a0

ai

z

P

, CHo

a0

ai

z

R and M

uz

aiz

### }

. Next we consider the situation that there is a region R in

E



R

### )

of Shape (ii). Region R could be spanned by an edge in

A

a0a1

alal+1

### }

. Then we mark this edge, that is, we compute

3

min

1

u

v

A

ai−1ai

: 1

i

l

1

### .

Otherwise R has more than two vertices in A

u

v

and we compute

4

min

1

u

v

A

ai+1

aj−1

M

M

1

subject to 0

i

j

l

1, j

i

2, CHo

a i

aj

P

, CHo

ai

aj

R and M

aiaj

### }

. Next we consider the situation that there is a region R in

E



R

### )

of Shape (iii), which is symmetric to Shape (i). Thus, we compute

5

min

1

u

w

A

ai+1

al

M

1

w

v

M

subject to 1

i

l, w

Pout

u

v

u

v

, v

w, CHo

ai

al

w

P

, CHo

ai

al

w

R and M

aiw

, as well as

6

min

3

u

v

A

ai+1

al

z

M

M

1

subject to 1

i

l, z

Pin

R, v

z, CHo

ai

al

z

P

, CHo

ai

al

z

R and M

aiz

v z

### }

. Finally, we need to make sure that the situation where there is a region R in

E



R

such that

A

ua1

val

B

R

### )

is treated correctly. Relying onObservation 2it suﬃces to compute

7

min

1

u

v

### 

(7)

an outer or an inner point. First we compute

1

min

1

u

w

A

ai+1

al

1

w

v

A

a1

ai

M

M

1

subject to w

Pout

u

v

u

v

, ai

w

ai+1, CHo

ai

ai+1

w

P

and M

wai+1

### }

. Then we compute

2

min

2

u

v

A

z

M

subject to z

Pin

R, ai

z

ai+1 and CHo

ai

ai+1

z

P

. This yields

R

min

1

2

### }

.

Type 2. For this type we know that there is a convex region R in

E

R

### )

that is spanned by the single edge aiajin M. We check all the possible locations for the vertex of R that we meet next after aj−1 when walking around the boundary of R in clockwise direction. We distinguish whether this vertex is an outer or an inner point. First we compute

1

min

1

u

w

A

aj

al

1

w

v

A

a1

aj−1

M

M

1

subject to w

Pout

u

v

u

v

, aj−1

w

aj, CHo

ai

aj

w

P

, CHo

ai

aj

w

R and M

waj

### }

. Then we compute

2

min

2

u

v

A

z

M

subject to z

Pin

R, aj−1

z

aj, CHo

ai

aj

z

P

and CHo

ai

aj

z

### }) ⊆

R.

Finally, we consider the situation that the vertices of R are exactly the points

ai

aj

### }

, that is, we compute

3

1

u

v

A

1

We obtain

R

min

1

2

3

### }

.

Type 3. Relying on Observation 1we extend the monotone path

A

### )

to the left beyond a1. We distinguish whether we extend the path to an outer or an inner point. Let aj be the rightmost point in A with the property that aj

### ≺

u. First we compute

1

min

1

u

w

a1

aj

M1

1

w

v

A

M2

subject to w

Pout

u

v

u

v

, w

a1, M1

M

uaj

and M2

M

ajaj+1

### }

. Then we compute

2

min

3

u

v

A

z

M

: z

Pin

R

z

a1

This yields

R

min

1

2

### }

.

Type 4. Again relying onObservation 1we extend the monotone path

A

### )

to the right beyond al. We distinguish whether we extend the path to an outer or an inner point. First we compute

1

min

3

u

w

A

M1

1

w

v

a2

al

M2

subject to w

Pout

u

v

u

v

, al

w, M1

M

va2

and M2

M

va2

### }

. Then we compute

2

min

4

u

v

A

z

M

: z

Pin

R

al

z

This results in

R

min

1

2

### }

.

Now that we have formally described how to process each type of subproblem introduced in Section3.2it remains to argue that the algorithm implicit in this description will actually compute a minimum convex partition of a given point set

P and to analyze the running time. This will be done in the next section. 3.4. Analysis

During the description of the processing of a subproblem R for each of the types presented in Section3.3our primary concern was to make sure that we do not miss to check a way the ﬁxed minimum convex partition E

### |

R could be structured. Furthermore, we managed to organize the search for E

### |



R in such a way that during the processing of R we are led to subproblems only that have one of the four types described in Section3.3, too. Next we want to argue that our algorithm always terminates. To this end, it is helpful to express the recursive structure of the algorithm by a directed graph G. The vertices of G correspond to all the subproblems deﬁned on point set P . There is an edge in G directed from subproblem R1 to subproblem R2 if we are led to R2 when processing R1.

(8)

and R2 be any two subproblems such that there is an edge in G directed from R1to R2.

Case 1: R1hasType 1. If there is some edge in B

R1

### )

marked as spanning a convex region, that is the set M in the formal description of R1 is non-empty, then either region R2 has smaller area than region R1 or B

R1

### )

is properly contained in

B

R2

### )

. Otherwise, that is the set M in the formal description of R1is empty, either region R2has smaller area than region

R1 or B

R1

B

R2

### )

and there is some edge in B

R2

### )

marked as spanning a convex region.

Case 2: R1 hasType 2, 3or4. Then either region R2has smaller area than region R1 or B

R1

### )

is properly contained in

B

R2

### )

.

Hence, no matter through which directed edge we leave R1 we can never return to R1.

### 2

It is an immediate consequence ofLemma 2that our algorithm always terminates. The last step in the analysis consists of bounding the running time of our algorithm. We want to employ the dynamic programming technique to avoid re-computation of minimum convex partitions for subproblems. Then we can assume that we can look up the value

R

### )

for every subproblem R we have already processed in a table eﬃciently. Of course, such an approach pays off only if the number of subproblems deﬁned on point set P can be bounded appropriately.

Since subproblems are deﬁned (up to reﬂection on the x-

### /

y-axis and rotation by 180◦) by a 4-tuple

t

u

v

A

M

### )

it is suﬃcient to derive a bound on the number of these 4-tuples: There is only a constant number of types. There are O

n2

### )

possible ways to choose

u

v

### )

. There are 2k subsets of P

in. Given t,

u

v

### )

and A there are only O

k2

### )

possible choices for M. This gives an upper bound of O

2kk2n2

### )

on the number of subproblems we have to deal with. It is also an upper bound on the size of the table used for the dynamic programming.

Finally, we have to give a bound on the time needed to process a single subproblem R under the assumption that for each of the subproblems R , such that there is an edge in the graph G directed from R to R , we can look up

R

### )

in the dynamic programming table in time

n

k

### )

. As the size of the table is exponential in k, depending on the properties of the underlying model of computation, it might not be possible to achieve

n

k

O

1

### )

. We will use a two-dimensional array to access pairs of points in Pout in O

1

### )

time. To each entry in this array is then associated a dictionary data structure which gives us access to the subsets of Pinin O

log

2k

O

k

### )

time. Thus we assume

n

k

O

k

### )

.

It can be checked that during the processing of R we look up at most O

n

k2

### )

values in the table. It remains to give a bound on the time spent for ﬁnding out which values to look up. This means we have to perform some tests such as whether a given triangle with vertices in P contains points of P in its interior or whether a set of inner points is in convex position. These tests can be performed in O

k

### )

time. Together with the bound on the number of subproblems this yields a bound of O

2kk2n2

n

k2

k

### )

on the running time of our algorithm. Since k

### 

n this can be simpliﬁed to O

2kk4n3

### )

.

Now we can state our result. The n log n-term in the bound on the running time is for checking whether P contains any inner points at all.

Theorem 3. Given a set P of n points in the plane, we can compute solutions to MCP and MWCP for P in O

2kk4n3

n log n

time

using O

2kk2n2

### )

space. The parameter k is the number of inner points in P . 4. Conclusion

It is not hard to adapt our ﬁxed-parameter algorithm such that it will work in situations where in addition to the point set P a set of edges Einis given as part of the input and an optimal convex partition of P among those containing Einmust be computed. As for the problem MWT it seems unlikely that we can improve the 2k-term in the bound on the running time signiﬁcantly relying on a concept based on monotone paths only.

Acknowledgements

We would like to thank the anonymous referees for their many helpful comments on earlier versions of this paper.

References

 M. Bern, D. Eppstein, Approximation algorithms for geometric problems, in: D.S. Hochbaum (Ed.), Approximation Algorithms for NP-Hard Problems, PWS Publishing Company, Boston, 1997, pp. 296–345.

 C. Borgelt, M. Grantson, C. Levcopoulos, Fixed-parameter algorithms for minimum weight partitions, in: Proc. European Workshop on Computational Geometry, 2006, pp. 5–12.

 V.G. Deineko, M. Hoffmann, Y. Okamoto, G.J. Woeginger, The traveling salesman problem with few inner points, Operations Research Letters 34 (1) (2006) 106–110.

 R.G. Downey, M.R. Fellows, Parameterized Complexity, Springer, 1999.

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Discrete and Computational Geometry, in: Lecture Notes in Computer Science, vol. 3742, Springer, 2004, pp. 83–94.

 J. Guo, F. Hüffner, R. Niedermeier, A structural view on parameterizing problems: Distance from triviality, in: Proc. International Workshop on Param-eterized and Exact Computation, in: Lecture Notes in Computer Science, vol. 3162, Springer, 2004, pp. 162–173.

 M. Hoffmann, Y. Okamoto, The minimum weight triangulation problem with few inner points, Computational Geometry 34 (3) (2006) 149–158.  J.M. Keil, Decomposing a polygon into simpler components, PhD thesis, University of Toronto, 1983.

 J.M. Keil, J. Snoeyink, On the time bound for convex decomposition of simple polygons, International Journal of Computational Geometry and Applica-tions 12 (2002) 181–192.

 C. Levcopoulos, D. Krznaric, Quasi-greedy triangulations approximating the minimum weight triangulation, Journal of Algorithms 27 (1998) 303–338.  A. Lingas, The power of non-rectilinear holes, in: International Colloquium on Automata, Languages and Programming, in: Lecture Notes in Computer

Science, vol. 140, Springer, 1982, pp. 369–383.

 W. Mulzer, G. Rote, Minimum weight triangulation is NP-hard, in: Proc. Symposium on Computational Geometry, ACM Press, 2006, pp. 1–10.  R. Niedermeier, Invitation to Fixed-Parameter Algorithms, Oxford University Press, 2006.

 D.A. Plaisted, J. Hong, A heuristic triangulation algorithm, Journal of Algorithms 8 (1987) 405–437.

 A. Spillner, A faster algorithm for the minimum weight triangulation problem with few inner points, in: Proc. Algorithms and Complexity in Durham Workshop, KCL Publications, 2005, pp. 135–146.

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