The traveling salesman problem (TSP) is one of the most fundamental optimizationproblems. We consider the β-metric traveling salesman problem(Δβ-TSP), i.e., the TSPrestricted to graphs satisfying the β-triangle inequalityc({v,w}) ≤ β(c({v,u}) + c({u,w})),for some cost function c and any three vertices u,v,w.The well-known path matching Christofides algorithm (PMCA) guarantees an approximationratio of 3β2/2 and is the best known algorithm for theΔβ-TSP, for 1 ≤ β ≤ 2. Weprovide a complete analysis of the algorithm. First, we correct an error in the originalimplementation that may produce an invalid solution. Using a worst-case example, we thenshow that the algorithm cannot guarantee a better approximation ratio. The example canalso be used for the PMCA variants for the Hamiltonian path problem with zero and oneprespecified endpoints. For two prespecified endpoints, we cannot reuse the example, butwe construct another worst-case example to show the optimality of the analysis also inthis case.

A unit disk graph is the intersection graphof a family of unit disks in the plane.If the disks do not overlap, it is also a unit coin graph or penny graph.It is known that finding a maximum independent setin a unit disk graph is a NP-hard problem.In this work we extend this result to penny graphs.Furthermore, we prove that finding a minimum clique partitionin a penny graph is also NP-hard, and presenttwo linear-time approximation algorithms for the computation of clique partitions:a 3-approximation algorithm for unit disk graphsand a 2-approximation algorithm for penny graphs.

The HP model is one of the most popular discretized models for attacking the protein folding problem, i.e., for the computational prediction of the tertiary structure of a protein from its amino acid sequence. It is based on the assumption that interactions between hydrophobic amino acids are the main force in the folding process. Therefore, it distinguishes between polar and hydrophobic amino acids only and tries to embed the amino acid sequence into a two- or three-dimensional grid lattice such as to maximize the number of contacts, i.e., of pairs of hydrophobic amino acids that are embedded into neighboring positions of the grid. In this paper, we propose a new generalization of the HP model which overcomes one of the major drawbacks of the original HP model, namely the bipartiteness of the underlying grid structure which severely restricts the set of possible contacts. Moreover, we introduce the (biologically well-motivated) concept of weighted contacts, where each contact gets assigned a weight depending on the spatial distance between the embedded amino acids. We analyze the applicability of existing approximation algorithms for the original HP model to our new setting and design a new approximation algorithm for this generalized model.

Given a weighted undirected graph G = (V,E),a tree (respectively tour) cover of an edge-weighted graph is a set of edges which forms a tree (resp. closed walk) and covers every other edge in the graph. The tree (resp. tour) cover problem is of finding a minimum weight tree (resp. tour) cover of G. Arkin, Halldórsson and Hassin (1993) give approximation algorithms with factors respectively 3.5 and 5.5. Later Könemann, Konjevod, Parekh, and Sinha (2003) study the linear programming relaxations and improve both factors to 3. We describe in the first part of the paper a 2-approximation algorithm for the metric case of tree cover.In the second part, we will consider a generalized version of tree (resp. tour) covers problem which is to find a minimum tree (resp. tours) which covers a subset D ⊆ E of G.We show that the algorithms of Könemann et al.can be adapted for the generalized tree and tours covers problem with the same factors.

In on-line computation, the instance of the problem dealt is notentirely known from the beginning of the solution process, but itis revealed step-by-step. In this paper we deal with on-lineindependent set. On-line models studied until now for this problemsuppose that the input graph is initially empty and revealedeither vertex-by-vertex, or cluster-by-cluster. Here we present anew on-line model quite different to the ones already studied. Itassumes that a superset of the final graph is initially present(in our case the complete graph on the order n of the finalgraph) and edges are progressively removed until the achievementof the final graph. Next, we revisit the model introduced in[Demange, Paradon and Paschos, Lect. Notes Comput. Sci.1963 (2000)326–334] and study relaxations assuming that somepaying backtracking is allowed.

We consider a special packing-covering pair of problems. Thepacking problem is a natural generalization of finding a(weighted) maximum independent set in an interval graph, thecovering problem generalizes the problem of finding a (weighted)minimum clique cover in an interval graph. The problem pairinvolves weights and capacities; we consider the case of unitweights and the case of unit capacities. In each case we describea simple algorithm that outputs a solution to the packing problemand to the covering problem that are within a factor of 2 of eachother. Each of these results implies an approximative min-maxresult. For the general case of arbitrary weights and capacitieswe describe an LP-based (2 + ε)-approximation algorithm forthe covering problem. Finally, we show that, unlessP = NP, the covering problem cannot be approximated inpolynomial time within arbitrarily good precision.

We study hardness of approximating several minimaximal and maximinimal NP-optimizationproblems related to the minimum linear ordering problem (MINLOP). MINLOP is to find a minimum weight acyclic tournament in a given arc-weighted completedigraph. MINLOP is APX-hard but its unweighted version is polynomial timesolvable. We prove that MIN-MAX-SUBDAG problem, which is a generalization ofMINLOP and requires to find a minimum cardinality maximal acyclic subdigraphof a given digraph, is, however, APX-hard. Using results of Håstad concerning hardness of approximating independence number of a graph we then prove similar results concerning MAX-MIN-VC (respectively, MAX-MIN-FVS) which requires to find a maximum cardinality minimal vertex cover in a given graph (respectively,a maximum cardinality minimal feedback vertex set in a given digraph). We alsoprove APX-hardness of these and several related problems on various degree bounded graphs and digraphs.

This paper deals with lower bounds on the approximability of different subproblems of the TravelingSalesman Problem (TSP) which is known not to admit any polynomial time approximation algorithm in general(unless $\mathcal{P}=\mathcal{NP}$ ). First of all, we present an improved lower bound for the Traveling Salesman Problemwith Triangle Inequality, Delta-TSP for short. Moreover our technique, an extension of the method ofEngebretsen [11], also applies to the case of relaxed and sharpened triangle inequality,respectively, denoted $\Delta_\beta$ -TSP for an appropriate β. In case of theDelta-TSP, we obtain a lowerbound of $\frac{3813}{3812}-\varepsilon$ on the polynomial-time approximability (for any small $\varepsilon> 0$ ), compared to the previous bound of $\frac{5381}{5380}-\varepsilon$ in [11]. Incase of the $\Delta_\beta$ -TSP, for the relaxed case ( $\beta> 1$ ) we present a lower bound of $\frac{3803+10\beta}{3804+8\beta}-\varepsilon$ , and for the sharpened triangle inequality( $\frac{1}{2}< \beta< 1$ ), the lower bound is $\frac{7611+10\beta^2+5\beta}{7612+8\beta^2+4\beta}-\varepsilon$ . The latter result is of interestespecially since it shows that the TSP is $\mathcal{APX}$ -hard even if one comes arbitrarily close to the trivialcase that all edges have the same cost.