Recognizing when heuristics can approximate minimum vertex covers is complete for parallel access to NP

DOI: 10.1051/ita:2005041

RECOGNIZING WHEN HEURISTICS CAN APPROXIMATE MINIMUM VERTEX COVERS IS

COMPLETE FOR PARALLEL ACCESS TO NP

Edith Hemaspaandra

, J¨ org Rothe

and Holger Spakowski

Abstract. For both the edge deletion heuristic and the maximum- degree greedy heuristic, we study the problem of recognizing those graphs for which that heuristic can approximate the size of a minimum vertex cover within a constant factor ofr, where r is a fixed rational number. Our main results are that these problems are complete for the class of problems solvable viaparallel access to NP. To achieve these main results, we also show that the restriction of the vertex cover problem to those graphs for which either of these heuristics can find an optimal solution remains NP-hard.

Mathematics Subject Classification.68Q15, 68Q17.

1. Introduction

The minimum vertex cover problem is the problem of finding in a given graph a smallest possible set of vertices that covers at least one vertex of each edge. The decision version of the minimum vertex cover problem,VC, is one of the standard NP-complete problems [5]. To cope with the intractability that appears to be in- herent to this problem, various heuristics for finding minimum vertex covers have been proposed. Two of the most prominent such heuristics are theedge deletion

Keywords and phrases. Computational complexity, completeness, minimum vertex cover heuristics, approximation, parallel access to NP.

∗This work was supported in part by the NSF under grant NSF-CCR-0311021, by the NSF and the DAAD under grant NSF-INT-9815095/DAAD-315-PPP-g¨u-ab, and by the DFG under grant RO 1202/9-1 and a Heisenberg Fellowship.

1Department of Computer Science, Rochester Institute of Technology, Rochester, NY 14623, USA;[email protected]

2Institut f¨ur Informatik, Heinrich-Heine-Universit¨at D¨usseldorf, 40225 D¨usseldorf, Germany;

[email protected]; [email protected]

c EDP Sciences 2005

Article published by EDP Sciences and available at http://www.edpsciences.org/itaor http://dx.doi.org/10.1051/ita:2005041

heuristic and the maximum-degree greedy heuristic, see, e.g., [20, 21]. These al- gorithms run in linear time and, depending on the structure of the given input graph, may find a minimum vertex cover, or may provide a good approximation of the optimal solution.

It is common to evaluate heuristics for optimization problems by analyzing their worst-case ratio for approximating the optimal solution. In this regard, the two heuristics considered behave quite differently: the edge deletion heuristic always approximates the size of a minimum vertex cover within a factor of 2 and thus achieves the best approximation ratio known, whereas the maximum-degree greedy heuristic, in the worst case, can have an approximation ratio as bad as logarithmic in the input size. The latter result follows from the early analysis of the approximation behavior of the greedy algorithm for the minimum set cover problem that was done by Johnson [14], Lov´asz [18], and Chv´atal [2] (who studied the weighted version of minimum set cover). Note that the vertex cover problem is the special case of the set cover problem, restricted so that each element occurs in exactly two sets. More recently, building on the work of Lund and Yannakakis [19], Feige [4] showed that, unless NP has slightly superpolynomial-time algorithms, the set cover problem cannot be approximated within (1−) lnn, where >0 and ln denotes the natural logarithm.

In this paper, we study the problem of recognizing those input graphs for which either of the two heuristics can approximate the size of a minimum vertex cover within a constant factor ofr, wherer≥1 is a fixed rational number. LetS_r^EDand S_r^MDG, respectively, denote this recognition problem for the edge deletion heuristic and for the maximum-degree greedy heuristic. Our main results are:

• For each rational number r with 1 ≤ r < 2, S_r^ED is P^NP -complete (see Th. 3.2).

• For each rational number r ≥ 1, S_r^MDG is P^NP -complete (see Ths. 4.3 and 4.4).

Here, P^NP denotes the class of problems that can be decided in polynomial time by parallel (i.e., truth-table) access to NP. Papadimitriou and Zachos [22]

introduced this class under the name P^NP[O(logn)], where “[O(logn)]” denotes that at most logarithmically many Turing queries are made to the NP oracle.

Hemaspaandra [7] proved that P^NP[O(logn)]= P^NP , and in fact many more char- acterizations of P^NP are known [17,27]. Other natural P^NP -hard and P^NP -complete problems can be found in the papers by Wagner [26], K¨obleret al.[17], Krentel [16], Kadin [15], Eiter and Gottlob [3], Hemaspaandra et al. [9, 11–13], and Rothe et al.[23, 24]; see also the surveys [8, 10].

The type of recognition problem studied in this paper was investigated for other problems and other heuristics as well. Bodlaender, Thilikos, and Yamazaki [1]

defined and studied the analogous problem for the independent set problem and the minimum-degree greedy heuristic, which they denoted byS_r. They proved thatS_r is coNP-hard and belongs to P^NP. Closing the gap between these lower and upper bounds, Hemaspaandra and Rothe [11] proved thatS_ris P^NP -complete. As in [11],

we obtain P^NP -hardness by reducing from a problem (namely,VCgeq, see Sect. 2) that can be shown to be P^NP -complete using the techniques of Wagner [26]. Also, we show that the vertex cover problem, restricted to those input graphs for which the heuristics considered can find an optimal solution, remains NP-hard. We then lift these NP lower bounds to P^NP lower bounds that prove our main results. This lifting requires a padding technique such that the given approximation ratio ris precisely met. In particular, to achieve P^NP -hardness of S_r^MDG for each rational numberr≥1, we modify a construction by Papadimitriou and Steiglitz [21] that they use to analyze the worst-case approximation behavior of the maximum-degree greedy heuristic.

2. Two heuristics for the vertex cover problem

We use the following notation. Fix the two-letter alphabet Σ ={0,1}. Σ^∗ is the set of all strings over Σ. Let·,·: Σ^∗×Σ^∗→ Σ^∗be a standard pairing function.

For any setL, letLdenote the number of elements ofL.

All graphs considered in this paper are undirected nonempty, finite graphs with- out multiple or reflexive edges. For any graphG, letV(G) denote the set of vertices of G, and let E(G) denote the set of edges of G. For any vertex v ∈V(G), the degree of v (denoted bydeg_G(v)) is the number of vertices adjacent to v in G; if Gis clear from the context, we omit the subscript and simply write deg(v). Let max-deg(G) = max_v∈V(G)deg(v) denote the maximum degree of the vertices of graphG. LetG and H be two disjoint graphs. The disjoint union of G and H is defined to be the graph U = G∪H with vertex set V(U) = V(G)∪V(H) and edge set E(U) = E(G)∪E(H). The join of G and H is defined to be the graph J = G H with vertex set V(J) = V(G)∪V(H) and edge set E(J) =E(G)∪E(H)∪ {{x, y} |x∈V(G) ∧ y∈V(H)}.

For any graph G, a subset C ⊆ V(G) is a vertex cover of G if for all edges {v, w} ∈E(G), {v, w} ∩C =∅. A vertex cover is said to be a minimum vertex cover ofGif it is of minimum size. For any graphG, letmvc(G) denote the size of a minimum vertex cover ofG. The vertex cover problem (VC, for short; see [5]) is defined to be the set of all pairsG, ksuch thatGis a graph,ka positive integer, andmvc(G)≤k.

All hardness and completeness results in this paper are with respect to the polynomial-time many-one reducibility, denoted ≤^pm. For sets Aand B, we say A≤^pmB if and only if there exists a polynomial-time computable functionf such that for all inputsx∈Σ^∗,x∈Aif and only iff(x)∈B.

We consider the following two heuristics (see,e.g., [20, 21]) for finding a mini- mum vertex cover of a given graph:

Edge Deletion Heuristic (ED): Given a graphG, the algorithm outputs a vertex coverC ofG. Initially, Cis the empty set. Nondeterministically choose an edge{u, v} ∈ E(G), add both u and v to C, and delete u, v, and all edges incident touand v from G. Repeat until there is no edge left inG.

Maximum-Degree Greedy Heuristic (MDG): Given a graphG, the al- gorithm outputs a vertex cover C of G. Initially, C is the empty set.

Nondeterministically choose a vertexv∈V(G) of maximum degree, addv toC, and deletev and all edges incident tov fromG. Repeat until there is no edge left inG.

As mentioned in the introduction, these two heuristics have a quite different ap- proximation behavior. While the worst-case ratio of the MDG algorithm is log- arithmic in the input size [14, 20], the ED algorithm always approximates the optimal solution within a factor of 2. Thus, despite its extreme simplicity, the edge deletion heuristic achieves the best approximation ratio known for finding minimum vertex covers [20].

The central question raised in this paper is: How hard is it to determine for which graphsGeither of these two heuristics can approximate the minimum vertex cover ofGwithin a factor ofr, for a given rational numberr≥1? Letmin-ed(G) (respectively,min-mdg(G)) denote the minimum size of the output set of the ED algorithm (respectively, of the MDG algorithm) on inputG, where the minimum is taken over all possible sequences of nondeterministic choices the algorithms can make. For any fixed rational r ≥ 1, S_r^ED (respectively, S_r^MDG) is the class of graphs for which ED (respectively, MDG) can output a vertex cover of size at mostrtimes the size of a minimum vertex cover. Formally,

S_r^ED = {G|Gis a graph andmin-ed(G)≤r·mvc(G)};

S_r^MDG = {G|Gis a graph andmin-mdg(G)≤r·mvc(G)}.

We will prove that for each fixed rational numberrwith 1≤r <2,S_r^EDis P^NP - complete, and that for each fixed rational numberr≥1,S_r^MDG is P^NP -complete.

To this end, we give reductions from the problemVCgeq, which is defined by VCgeq={G, H |GandH are graphs such thatmvc(G)≥mvc(H)}.

It is known thatVCgeq is P^NP -complete, cf. Wagner [26]. A reduction from any problem in P^NP toVCgeqthat in addition has some useful properties (see Lem. 2.1 below) can easily be obtained using the techniques of Wagner [26]; see Theorem 12 from [25] for an explicit proof of Lemma 2.1.

Lemma 2.1 (cf.[25, 26]). For any set X ∈P^NP , there exists a polynomial-time computable functionf that reducesX toVCgeqin such a way that for eachx∈Σ^∗, f(x) =G, H is an instance ofVCgeqand

x∈X =⇒ mvc(G) =mvc(H);

x∈X =⇒ mvc(G)<mvc(H).

3. The edge deletion heuristic

Lemma 3.1 below states that the vertex cover problem restricted to graphs inS1^ED

is NP-hard. The reductiongfrom Lemma 3.1 will be used in the proof of the main result of this section, Theorem 3.2. Define the problem

VC-S1^ED={G, k |G∈ S1^ED andk∈N⁺ andmvc(G)≤k}.

Lemma 3.1. There exists a polynomial-time many-one reduction g from VC to VC-S1^ED transforming any given graph Ginto a graph H∈ S1^ED such that

mvc(H) = 2(mvc(G) +V(G)). (1)

Hence,VC-S1^ED isNP-hard.¹

Proof. Given any graph G, we construct the graph H ∈ S1^ED as follows. For each vertex v ∈ V(G), create a component G_v that is defined by the vertex set V(G_v) ={v₁, v₂, v₃, v₄} and the edge setE(G_v) ={{v₁, v₂},{v₃, v₄},{v₁, v₃}}.

Define the graph H by joining every pair of components that correspond to adjacent vertices ofG:

V(H) =

v∈V(G)

V(G_v);

E(H) = {{a_i, b_j} | {a, b} ∈E(G) andi, j∈ {1,2,3,4}} ∪

v∈V(G)

E(G_v).

We now prove equation (1). LetCbe a minimum vertex cover ofG,i.e.,mvc(G) = C. Construct a vertex coverD of H as follows. For each vertex v ∈ C, add v1, v2, v3, andv4 toD; and for each vertexw∈V(G)−C, addw1 andw3 toD.

Hence,

D= 2(C+V(G)).

Sincemvc(H)≤ D, it follows that

mvc(H)≤2(mvc(G) +V(G)).

Conversely, letDbe a minimum vertex cover ofH,i.e.,mvc(H) =D. Then, it holds that:

• for each edge{u, v} ∈E(G),V(G_u)⊆D orV(G_v)⊆D;

• for each vertexv∈V(G),D∩V(G_v) ≥2.

Hence,

D ≥ 4·mvc(G) + 2(V(G) −mvc(G))

= 2(mvc(G) +V(G)).

1Theorem 3.2 will imply thatVC-S^ED1 in fact is P^NP -complete.

It follows that

mvc(H)≥2(mvc(G) +V(G)), which proves equation (1).

It remains to prove that H ∈ S1^ED. Let C be a minimum vertex cover ofG.

The edge deletion algorithm can find a vertex cover of H as follows. For every vertex v ∈C, choose the edges{v₁, v₂} and {v₃, v₄}. For the remaining vertices w∈V(G)−C, choose the edge{w₁, w₃}. Thus,min-ed(H) = 2(mvc(G)+V(G)).

By equation (1),min-ed(H) =mvc(H), so H∈ S1^ED. Theorem 3.2. For each rational numberrwith1≤r <2,S_r^ED isP^NP -complete.

Proof. It is easy to see that S_r^ED is in P^NP . To prove P^NP -hardness, let X be an arbitrary set in P^NP , and let f be the reduction from X to VCgeq stated in Lemma 2.1. Fix any rational number r with 1 ≤ r < 2, and let and m be integers such thatr=/m. Note that 1≤m≤ <2m.

For any stringx∈Σ^∗, letf(x) =G1, G2. Since we can add isolated vertices to any graphGwithout alteringmvc(G), we may without loss of generality assume thatV(G1)=V(G2). Letgbe the reduction from Lemma 3.1 that transforms any given graph G into a graph H ∈ S1^ED such that equation (1) holds. Let H1 = g(G1) and H2 = g(G2). Thus, both H1 and H2 are in S1^ED, and for i∈ {1,2}, we havemvc(H_i) = 2(mvc(G_i) +V(G_i)).

We will define a graphH and an integer k≥0 such that:

min-ed(H) = r(m·mvc(H2) + 2km); (2) mvc(H) = m·mvc(H1) + 2km. (3) The reduction mapping any given stringx(via the pairG1, G2obtained ac- cording to Lem. 2.1 and via the pair H1, H2 obtained according to Lem. 3.1) to the graph H such that equations (2) and (3) are satisfied will establish that X≤^pmS_r^ED. In particular, from these equations, we have that:

• mvc(H₂) =mvc(H₁) implies min-ed(H) = r·mvc(H), and

• mvc(H2)>mvc(H1) implies min-ed(H) > r·mvc(H).

Note that, due to Lemma 2.1,mvc(H2)≥mvc(H1).

Look at Figure 1 for the construction ofH fromH₁andH₂. The graphH con- sists of two subgraphs,LandR, that are joined by the join operation, plus some ad- ditional vertices and edges that are connected toR. Formally, letH₁¹, H₁², . . . , H₁^m bempairwise disjoint copies ofH₁, and letH₂¹, H₂², . . . , H₂ bepairwise disjoint copies ofH₂. Letk=V(H₂)+mV(H₁). LetI₁ andI₂ be independent sets such thatL contains exactly k(2m−) vertices andR exactlykvertices. (This is possible, becausek(2m−)−V(H₂)is not negative, since 2m−≥1, and k−mV(H1) is not negative, since ≥ 1.) Let e_i = {a_i, b_i} (1≤ i≤ k) be additional edges. Every vertexa_i is adjacent to exactly one vertex inR, and each vertex inR is adjacent to exactly one vertexa_i. The vertices a_i and b_i are not adjacent to any other vertices.

H

H

· · · H

H

· · ·

L R

· · ·

indep. set I

indep. set I

H

H

a

a

a

b

b

b

Figure 1. The graphH constructed fromH₁ andH₂.

(1) We first determinemin-ed(H). LetE be a fixed minimum-size output set of the ED algorithm on input H, i.e., min-ed(H) = E. Since E is a vertex cover ofH,E must containa_i orb_i for eachi∈ {1, . . . , k}. Since the ED algorithm can delete only edges, andE is a minimum-size output set, it follows thatE contains all verticesa_i, all vertices from R, and no vertexb_i.

Let C_L be a minimum-size output set of the ED-algorithm on input L. By construction ofL, C_L=·min-ed(H2). Thus, since H2∈ S1^ED, C_L=·mvc(H₂).

Define E = V(R)∪C_L ∪_k

i=1{a_i}. It is easy to see that E is a minimum-size output set of the ED algorithm on inputH. Hence,

min-ed(H) = 2k+·mvc(H₂)

= r(2km+m·mvc(H2)).

This proves equation (2).

(2) We now determinemvc(H). LetC be a fixed minimum vertex cover ofH, i.e.,mvc(H) =C. Distinguish the following two cases.

Case 1. V(R) ⊆ C. In this case, C contains all vertices from R, at least one of a_i or b_i for each i, 1 ≤i ≤ k, and a minimum vertex cover ofL. Hence,

mvc(H) = 2k +·mvc(H2).

Case 2. V(L) ⊆ C. In this case, C contains all vertices from L, a minimum vertex cover of R, and exactly one of a_i or b_i for each i, 1≤i≤k. Hence,

mvc(H) = k(2m−) +k+m·mvc(H₁)

= 2km+m·mvc(H1).

Sincemvc(H1)≤mvc(H2),m≤, and 2km≤2k, it follows that mvc(H) = 2km+m·mvc(H1).

This proves equation (3).

The proof of Theorem 3.2 is complete.

4. The maximum-degree greedy heuristic

Lemma 4.1 below states that the vertex cover problem restricted to graphs in S1^MDGis NP-hard. The proof of Lemma 4.1 is reminiscent of a proof by Bodlaender et al. (see Th. 4 from [1]), who show that the independent set problem restricted to graphs for which the minimum-degree greedy heuristic can find an optimal solution is NP-hard. The reductiongfrom Lemma 4.1 will be used in the proof of the main result of this section, Theorem 4.4. Define the problem

VC-S1^MDG={G, k |G∈ S1^MDG andk∈N⁺ andmvc(G)≤k}.

Lemma 4.1. There is a polynomial-time many-one reductiongfromVCtoVC-S1^MDG

transforming any given graph Ginto a graphH ∈ S1^MDG such that

mvc(H) =mvc(G) +E(G)(max-deg(G) + 1). (4) Hence,VC-S1^MDG isNP-hard.²

Proof. Given any graph G, we construct the graph H ∈ S1^MDG as follows. Let

∆ =max-deg(G). Replace each edge of Gby a gadget that contains a complete bipartite graph of size 2(∆ + 1). Formally,H is defined by:

V(H) = V(G)∪

e={u,v} ∈E(G)

({u^e_i|1≤i≤∆ + 1} ∪ {v^e_i|1≤i≤∆ + 1}) ;

E(H) =

e={u,v} ∈E(G)

{{u^e_i, v^e_j} |1≤i, j≤∆ + 1} ∪ {{u, u^e₁}} ∪ {{v, v₁^e}}

.

We now prove equation (4). Let C be a minimum vertex cover of G, i.e., mvc(G) =C. Note that{u, v} ∩C=∅for each edge{u, v}inE(G). Construct a vertex coverD ofH as follows:

• D contains all vertices fromC.

• For every edgee={u, v}inE(G), add toD:

– either all verticesu^e_i, 1≤i≤∆ + 1, ifu∈C or if bothuandv are in C;

– or all verticesv^e_i, 1≤i≤∆ + 1, ifv∈C.

2Theorem 4.3 will imply thatVC-S^MDG1 in fact is P^NP -complete.

It follows that

mvc(H)≤mvc(G) +E(G)(∆ + 1).

Conversely, letD be a minimum vertex cover ofH,i.e., mvc(H) =D. Con- struct a vertex coverC of Gas follows. Initially, set C =D. Let e={u, v} be any fixed edge in E(G). Suppose that at least one vertex from {u, v} is in D.

SinceDis a vertex cover ofH, it contains at least ∆ + 1 of the verticesu^e_i andv_i^e, 1 ≤i≤∆ + 1, that correspond to the edge e. Remove any ∆ + 1 such vertices fromC. Suppose now that neitherunorvis inD. SinceDis a vertex cover ofH, it contains at least ∆ + 2 of the verticesu^e_i andv_i^e, 1≤i≤∆ + 1, that correspond to the edgee. Remove any ∆ + 2 such vertices fromC, and add toC one ofuor vinstead. Since the set C thus obtained is a vertex cover ofG, we have

mvc(H)≥mvc(G) +E(G)(∆ + 1),

which proves equation (4).

It remains to prove thatH ∈ S1^MDG. LetC be a minimum vertex cover ofG.

The maximum-degree greedy algorithm can find a vertex cover of H as follows.

For every edgee={u, v}inE(G), the MDG algorithm on inputH can choose:

• either all verticesu^e_i, 1≤i≤∆ + 1, ifu∈C or if bothuandv are inC;

• or all verticesv_i^e, 1≤i≤∆ + 1, ifv∈C.

Note that the MDG heuristic can always do so, since every vertex in V(G) has degree at most ∆. Subsequently, all vertices that are not inC are isolated. Thus, the MDG algorithm can now choose all vertices fromC. Hence,

min-mdg(H) =mvc(G) +E(G)(∆ + 1).

By equation (4),min-mdg(H) =mvc(H), so H∈ S1^MDG.

Lemma 4.2 below will be used in the proof of Theorem 4.4. The construc- tion of the graph G in this lemma is a modification of a construction given by Papadimitriou and Steiglitz [21, p. 408, Fig. 17-3], which shows that the worst- case approximation ratio of the MDG heuristic can be as bad as logarithmic in the input size, and so grows unboundedly. Similar constructions for achieving the worst-case approximation behavior of the greedy heuristic solving the more general minimum set cover problem were given by Johnson [14], Lov´asz [18], and Chv´atal [2].

Lemma 4.2. For all positive integers n₁,n₂,δ≥6, andµ satisfying

µ(lnµ−2 ln(δ+ 2)−1)≥n₁+n₂, (5)

there exists a bipartite graph Gwith the following properties:

(1) V(G) =V ∪V˜ such that V ∩V˜ =∅ and both V and V˜ are independent sets, where

• V ={u1, u2, . . . , u_n1, w1, w2, . . . , w_µ, z1, z2, . . . z_n2}and

• V˜ ={u˜1,u˜2, . . . ,u˜_n1,w˜1,w˜2, . . . ,w˜_µ}.

(2) {{u_i,u˜_i} |1≤i≤n₁} ∪ {{w_i,w˜_i} |1≤i≤µ} ⊆E(G).

(3) Every vertexu˜_i, where1≤i≤n₁, has degree one.

(4) For each induced subgraphS ofGthat can be obtained by deleting vertices fromV such that V ∩V(S)=∅, it holds that

v∈Vmax∩V(S)deg_S(v)>max

v∈V˜ deg_S(v) +δ.

Proof. Let the constants n1, n2, δ, and µ be given such that equation (5) is satisfied. We describe the construction of the graphG. As stated in the lemma, the vertex set ofG is given by V(G) = V ∪V˜, whereV and ˜V are two disjoint independent sets. Rename the vertices ofV byV ={α1, α2, . . . , α_n1+µ+n2}.

Let ˜W ={w˜₁,w˜₂,w˜₃, . . . ,w˜_µ}. The edge set ofGis defined as follows:

• Create the edges{u_i,u˜_i}for eachiwith 1≤i≤n₁and the edges{w_j,w˜_j} for eachj with 1≤j≤µ.

• Partition ˜W into µ

δ+3

disjoint sets ˜W₁^δ+3,W˜₂^δ+3, . . . ,W˜^δ+3δ+3^µ of sizeδ+ 3 each, possibly leaving out some vertices from ˜W and taking care that no vertex in ˜W_i^δ+3 already is connected withα_i, 1≤i ≤

δ+3µ

. For each i with 1≤i≤

δ+3µ

, connect α_i with each vertex in ˜W_i^δ+3 by an edge.

• Partition ˜W into µ

δ+4

disjoint sets ˜W₁^δ+4,W˜₂^δ+4, . . . ,W˜^δ+4δ+4^µ of sizeδ+ 4 each, possibly leaving out some vertices from ˜W and taking care that no vertex in ˜W_i^δ+4 already is connected with αδ+3^{µ +i}, 1≤i≤

δ+4µ

. For eachi with 1 ≤i ≤

δ+4µ

, connect αδ+3^{µ +}i with each vertex in ˜W_i^δ+4 by an edge.

• Partition ˜W into µ

δ+5

disjoint sets ˜W₁^δ+5,W˜₂^δ+5, . . . ,W˜^δ+5

δ+5^µ of sizeδ+ 5 each, possibly leaving out some vertices from ˜W and taking care that no vertex in ˜W_i^δ+5already is connected withαδ+3^{µ +}δ+4^{µ +}i, 1≤i≤

δ+5µ

. For eachiwith 1≤i≤

δ+5µ

, connectαδ+3^{µ +}δ+4^{µ +i} with each vertex in ˜W_i^δ+5 by an edge.

• Continue in this way until all verticesα_iare connected with vertices in ˜W. The construction is possible, since equation (5) implies

µ δ+ 3 +

µ

δ+ 4 +· · ·+ µ

µ−1 ≥n1+µ+n2, (6)

and thus there are enough vertices in ˜W. To see why, note that µ

δ+ 3 + µ

δ+ 4 +· · ·+ µ

µ−1

= µ

1

+ µ

2

+· · ·+ µ

µ −1−µ 1

+ µ

2

+· · ·+ µ

δ+ 2

≥ µ 1 +µ

2 +· · ·+µ µ−µ−

µ 1 +µ

2 +· · ·+ µ δ+ 2

= µH_µ−µ−µH_δ+2

≥ µlnµ−µ−µln(δ+ 2)−µ

≥ µlnµ−µln(δ+ 2)−2µ

≥ µlnµ−µln(δ+ 2)−µln(δ+ 2) (7)

= µlnµ−2µln(δ+ 2).

Here,H_k denotes thekth harmonic number, which is defined byH_k =_k

i=1 1 i. It is well known that for allk, lnk≤H_k ≤lnk+ 1 (see, e.g., Graham, Knuth, and Patashnik [6]). Equation (7) holds becauseδ≥6 and hence ln(δ+ 2)≥2.

It is evident from the construction thatG has all the required properties. In particular, to see why Property 4 holds, let S be any induced subgraph of G that can be obtained by deleting vertices from V such that V ∩V(S) =∅. Let y_S = max_v∈V∩V(S)deg_S(v). By construction,S can have only edges of the form {u_i,u˜_i}or{w_j,w˜_j} or edges that are added during the stagesδ+ 3, δ+ 4, . . . , y_S, whereδ+idenotes the stage in which ˜W is partitioned into subsets of sizeδ+i.

It follows that

maxv∈V˜ deg_S(v)≤1 +y_S−(δ+ 3) + 1 =y_S−δ−1< y_S−δ,

which proves the lemma.

Theorem 4.3. S1^MDG isP^NP -complete.

Proof. It is easy to see thatS1^MDG is in P^NP . To prove P^NP -hardness ofS1^MDG, let X be an arbitrary set in P^NP , and letf be the reduction from X toVCgeqstated in Lemma 2.1. For any stringx∈Σ^∗, letf(x) =G₁, G₂.

We will define a graphGand an integer q≥0 such that:

min-mdg(G) = mvc(G₂) +q; (8)

mvc(G) = mvc(G1) +q. (9)

The reduction mapping any given stringx(via the pairG₁, G₂obtained ac- cording to Lem. 2.1) to the graphG such that equations (8) and (9) are satisfied

H

a

a

· · · a

b

b

b

L R = G

Figure 2. The graphG constructed fromG1 andH2.

will establish thatX≤^pmS1^MDG. In particular, from these equations, we have that:

• mvc(G2) =mvc(G1) impliesmin-mdg(G) = mvc(G), and

• mvc(G₂)>mvc(G₁) impliesmin-mdg(G) >mvc(G).

Note that, due to Lemma 2.1,mvc(G2)≥mvc(G1).

We now describe the construction ofG. Let gbe the reduction from Lemma 4.1 and letH2=g(G2). Thus,H2 is inS1^MDGand, by equation (4),

mvc(H2) =mvc(G2) +E(G2)(max-deg(G2) + 1). (10) Since one can add isolated vertices to any graphGwithout affecting the values of mvc(G) ormin-mdg(G), we may without loss of generality assume that

V(H₂)=V(G₁)+E(G₂)(max-deg(G2) + 1). (11)

Look at Figure 2 for the construction of G from G1 and H2. The graph G consists of two subgraphs, L and R, that are joined by the join operation, plus some additional vertices and edges that are connected toL. Formally, choose 2j new verticesa_iandb_i, 1≤i≤j, wherejis a fixed integer large enough such that the degree of each vertex inRis larger than the maximum degree of the vertices inL. Note that the degree of each vertex inRmust remain larger than the degree of any vertex inLeven after some vertices have been removed fromR.

LetB be the bipartite matching with the vertex set V(B) ={a_i|1≤i≤j} ∪ {b_i|1≤i≤j}

and the edge setE(B) = {{a_i, b_i} |1 ≤ i ≤j}. Let R = G₁, and let L be the graph with the vertex set V(L) = {a_i|1 ≤ i ≤ j} ∪V(H₂) and the edge set E(L) = E(H2). The graph G is defined by forming the joinL R, i.e., there are edges connecting each vertex ofL with each vertex of R, plus attaching the verticesb_i, 1≤i≤j, toLby adding thej edges fromE(B).

We first considermin-mdg(G). By our choice of j, each vertex inR has a de- gree larger than the degree of any vertex not inR. Hence, on input G, the MDG algorithm first deletes all vertices fromR. Subsequently, it can find a minimum vertex cover ofH₂, which has sizemvc(G₂) +E(G₂)(max-deg(G2) + 1) by equa- tion (10), and eventually it can choose, say, the verticesa_i, 1≤i≤j, to cover the edges ofB. Hence,

min-mdg(G) = V(G1)+mvc(G2) +E(G2)(max-deg(G2) + 1) +j

(11)= mvc(G₂) +V(H₂)+j.

We now considermvc(G). Since every vertex cover of G must contain all vertices of L or all vertices of R to cover the edges connecting L and R, it follows from equations (10) and (11) that:

mvc(G) = min{V(G₁)+mvc(H₂) +j, V(H₂)+j+mvc(G₁)}

= min{mvc(G2) +V(H2)+j, mvc(G1) +V(H2)+j}. Sincemvc(G2)≥mvc(G1), it follows that

mvc(G) = mvc(G₁) +V(H₂)+j.

Hence, setting q=V(H₂)+j, equations (8) and (9) are satisfied, which com-

pletes the proof thatS1^MDG is P^NP -complete.

Theorem 4.4. For each rational numberr >1,S_r^MDG isP^NP -complete.

Proof. Fix any rational number r =/m, where and m are integers with 1 ≤ m < . Without loss of generality, we may assume that gcd(−m, m) = 1, where gcd(a, b) denotes the greatest common divisor of the integersaandb. It is easy to see thatS_r^MDGis in P^NP . To prove P^NP -hardness ofS_r^MDG, let X be an arbitrary set in P^NP , and letf be the reduction fromX toVCgeqstated in Lemma 2.1. For any stringx∈Σ^∗, letf(x) =G₁, G₂. Note thatmvc(G₂)≥mvc(G₁).

We will define a graphG_r and integersp, q≥0 such that:

min-mdg(G_r) = r(p·mvc(G₂) +q); (12) mvc(G_r) = p·mvc(G1) +q. (13) The reduction mapping any given stringx(via the pairG1, G2obtained ac- cording to Lem. 2.1) to the graphG_rsuch that equations (12) and (13) are satisfied will establish thatX≤^pmS_r^MDG. In particular, from these equations, we have that:

• mvc(G₂) =mvc(G₁) impliesmin-mdg(G_r) =r·mvc(G_r), and

• mvc(G2)>mvc(G1) impliesmin-mdg(G_r)> r·mvc(G_r).

We now describe the construction ofG_r:

• Let g be the reduction from Lemma 4.1 and let H2 = g(G2). Thus, H2∈ S1^MDG and equation (10) holds:

mvc(H2) =mvc(G2) +E(G2)(max-deg(G2) + 1).

• LetG¹₁, G²₁, . . . , G^m₁ be mpairwise disjoint copies ofG1, and letH₂¹, H₂², . . .,H₂ bepairwise disjoint copies ofH₂.

• Let ˜U=

i=1H₂ⁱ be the disjoint union of these copies ofH₂, and rename the vertices of ˜U byV( ˜U) ={u˜₁,u˜₂, . . . ,u˜_·V(H2)}.

• LetZ=_m

i=1Gⁱ₁ be the disjoint union of these copies ofG₁, and rename the vertices ofZ byV(Z) ={z₁, z₂, . . . , z_m·V(G1)}.

• To apply Lemma 4.2, choosen₁ =· V(H₂), n₂ ≥m· V(G₁), and δ= max{6,max-deg(H₂) + 1}, where the exact value ofn₂will be specified below. Choose the constantµso as to satisfy equation (5):

µ(lnµ−2 ln(δ+ 2)−1)≥n2+n1.

• Given the constantsn1,n2,δ, andµ, defineG_rto be the bipartite graphG from Lemma 4.2 extended by the edges between the ˜u_i vertices that were added above to represent the structure of the copies ofH2, and extended by the edges between thez_j vertices that were added above to represent the structure of the copies of G1. That is, unlike G, the graph G_r is no longer a bipartite graph. Formally, the vertex set ofG_ris given by

V(G_r) = V(G) =V ∪V ,˜ where

V = {u₁, u₂, . . . , u_n1, w₁, w₂, . . . , w_µ, z₁, z₂, . . . z_n2} and V˜ = {˜u₁,u˜₂, . . . ,u˜_n1,w˜₁,w˜₂, . . . ,w˜_µ},

and the edge set ofG_r is given byE(G_r) =E(G)∪E( ˜U)∪E(Z), where E(G) is constructed as in the proof of Lemma 4.2.

This completes the construction ofG_r. We now prove equations (12) and (13).

(1) We first consider min-mdg(G_r). By construction, for each vertexv in ˜V, we have

deg_G

r(v)≤deg_G(v) +max-deg(H2)<deg_G(v) +δ. (14) Let S be any induced subgraph ofG_r that can be obtained by deleting vertices fromV such that V ∩V(S)=∅. Property 4 of Lemma 4.2 and equation (14) imply that

v∈Vmax∩V(S)deg_S(v)>max

v∈V˜ deg_S(v).

Hence, on inputG_r, the MDG algorithm starts by choosing then1+µ+n2

vertices from V, which isolates each vertex ˜w_i ∈V˜ and leaves isolated

copies of H2. Subsequently, since H2 ∈ S1^MDG, the MDG algorithm can choose a minimum vertex cover in each of thesecopies ofH2. By equa- tion (10),

mvc(H2) =mvc(G2) +E(G2)(max-deg(G2) + 1), and hence,

min-mdg(G_r) =n₁+µ+n₂+(mvc(G₂) +E(G₂)(max-deg(G2) + 1)).

(2) We now consider mvc(G_r). Define the set C = ˜V ∪D, where D with D =m·mvc(G₁) is a minimum vertex cover of Z. It is obvious from the construction ofG_rthat C is a minimum vertex cover ofG_r. Hence,

mvc(G_r) =n₁+µ+m·mvc(G₁).

To complete the proof, we have to choosen2 ≥m· V(G1) such that equa- tions (12) and (13) are satisfied for suitable integerspand q. Settingp=mand q=n₁+µand requiring

n1+n2+µ+· E(G2)(max-deg(G2) + 1) =r(n1+µ) (15) or, equivalently,

m·n2+m·· E(G2)(max-deg(G2) + 1) = (−m)n1+ (−m)µ (16) satisfies equations (12) and (13).

Our assumption that gcd(−m, m) = 1 implies that the equation

m·n₂+ 1 = (−m)µ (17)

has integer solutions. Fix one such solution (n₂, µ). Multiplying this solution with m·· E(G2)(max-deg(G2) + 1)−(−m)n1, we obtain an integer solution (n2,µ) for equation (16). For everyk∈Z, (n2, µ) is a solution of equation (16), where

n2=n2+k(−m), (18)

µ=µ+km. (19)

Choosingklarge enough, we can make sure that (a)n₂andµare positive integers³, (b)n₂ ≥m· V(G₁), and (c)n₂ andµ satisfy equation (5) for givenn₁ and δ.

It is easy to see thatk can be small enough such thatn₂ andµare polynomially bounded in the size of the input of the reduction being described. This completes

the proof of the theorem.

3Recall that−m >0 andm >0.

In conclusion, we have shown that for both the edge deletion heuristic and the maximum-degree greedy heuristic, it is P^NP -complete to recognize those graphs for which the heuristic can approximate the size of a minimum vertex cover within a constant factor of r, where r is a fixed rational number. These results add new problems (S_r^ED andS_r^MDG) to the list of P^NP -complete problems previously obtained.

Acknowledgements. We thank Dieter Kratsch and Andreas Brandst¨adt for interesting discussions on graph theory and graph-theoretical notation. We thank the anonymous referees for their helpful comments.

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Communicated by V. Diekert.

Received July, 2004. Accepted February 10, 2005.

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