A 2-approximation algorithm for the network substitution problem

The network substitution problem is to substitute an existing network for a new network so that to minimize the cost of exploiting the existing network during the period when the new network is being constructed. We show that this problem is NP-hard, and propose a 2-approximation algorithm for solving it.     

A faster 2-approximation algorithm for the minmax p-traveling salesmen problem on a tree

Given an edge-weighted tree T and an integer p1, the minmax p-traveling salesmen problem on a tree T asks to find p tours such that the union of the p tours covers all the vertices. The objective is to minimize the maximum of length of the p tours. It is known that the problem is NP-hard and has a (2−2/(p+1))-approximation algorithm which runs in O(p^p−1n^p−1) time for a tree with n vertices. In this paper, we consider an extension of the problem in which the set of vertices to be covered now can be chosen as a subset S of vertices and weights to process vertices in S are also introduced in the tour length. For the problem, we give an approximation algorithm that has the same performance guarantee, but runs in O((p−1)!·n) time.     

2-approximation algorithm for minmax absolute maximum lateness scheduling-location problem

The problem of minmax absolute scheduling-location is investigated on trees with interval edge length. Jobs are located at vertices and must travel to the machine. The goal is to find a machine location and simultaneously a schedule to minimize the maximum lateness in the worst-case. We derive a result that could reduce the robust versions to deterministic problems. An efficient algorithm is developed to solve special cases. A 2-approximation algorithm is proposed for the robust problem on the underlying tree.     

Improved approximation of maximum vertex cover

We provide a new LP relaxation of the maximum vertex cover problem and a polynomial-time algorithm that finds a solution within the approximation factor , where is the size of the smallest clique in a given clique-partition of the edge weighting of G.     

Combinatorial 5/6-approximation of Max Cut in graphs of maximum degree 3

The best approximation algorithm for Max Cut in graphs of maximum degree 3 uses semidefinite programming, has approximation ratio 0.9326, and its running time is Θ(n^3.5logn); but the best combinatorial algorithms have approximation ratio 4/5 only, achieved in O(n²) time [J.A. Bondy, S.C. Locke, J. Graph Theory 10 (1986) 477–504; E. Halperin, et al., J. Algorithms 53 (2004) 169–185]. Here we present an improved combinatorial approximation, which is a 5/6-approximation algorithm that runs in O(n²) time, perhaps improvable even to O(n). Our main tool is a new type of vertex decomposition for graphs of maximum degree 3.     

An edge-reduction algorithm for the vertex cover problem

An approximation algorithm for the vertex cover problem is proposed with performance ratio on special graphs. On an arbitrary graph, the algorithm guarantees a vertex cover S₁ such that where S^∗ is an optimal cover and ξ is an error bound identified.     

Np-hardness proof and an approximation algorithm for the minimum vertex ranking spanning tree problem

The minimum vertex ranking spanning tree problem (MVRST) is to find a spanning tree of G whose vertex ranking is minimum. In this paper, we show that MVRST is NP-hard. To prove this, we polynomially reduce the 3-dimensional matching problem to MVRST. Moreover, we present a (⌈D_s/2⌉+1)/(⌊log₂(D_s+1)⌋+1)-approximation algorithm for MVRST where D_s is the minimum diameter of spanning trees of G.     

A 12/7-approximation algorithm for the discrete Bamboo Garden Trimming problem

We study the discrete Bamboo Garden Trimming problem (BGT), where we are given n bamboos with different growth rates. At the end of each day, one can cut down one bamboo to height zero. The goal in BGT is to make a perpetual schedule of cuts such that the height of the tallest bamboo ever is minimized. Here, we improve the current best approximation guarantee by designing a 12/7-approximation algorithm.     

A list heuristic for vertex cover

We analyze a list heuristic for the vertex cover problem that handles the vertices in a given static order based on the degree sequence. We prove an approximation ratio of at most for a nonincreasing degree sequence, and show that no ordering can achieve an approximation ratio of less than .     

A 2-approximation algorithm for interval data minmax regret sequencing problems with the total flow time criterion

In this paper we discuss a minmax regret version of the single-machine scheduling problem with the total flow time criterion. Uncertain processing times are modeled by closed intervals. We show that if the deterministic problem is polynomially solvable, then its minmax regret version is approximable within 2.     

The satisfactory partition problem

The SATISFACTORY PARTITION problem consists in deciding if a given graph has a partition of its vertex set into two nonempty parts such that each vertex has at least as many neighbors in its part as in the other part. This problem was introduced by Gerber and Kobler [Partitioning a graph to satisfy all vertices, Technical report, Swiss Federal Institute of Technology, Lausanne, 1998; Algorithmic approach to the satisfactory graph partitioning problem, European J. Oper. Res. 125 (2000) 283-291] and further studied by other authors but its complexity remained open until now. We prove in this paper that SATISFACTORY PARTITION, as well as a variant where the parts are required to be of the same cardinality, are NP-complete. However, for graphs with maximum degree at most 4 the problem is polynomially solvable. We also study generalizations and variants of this problem where a partition into k nonempty parts (k?3) is requested.     

Approximating the maximum 3-edge-colorable subgraph problem

We offer the following structural result: every triangle-free graph G of maximum degree 3 has 3 matchings which collectively cover at least of its edges, where γ_o(G) denotes the odd girth of G. In particular, every triangle-free graph G of maximum degree 3 has 3 matchings which cover at least 13/15 of its edges. The Petersen graph, where we can 3-edge-color at most 13 of its 15 edges, shows this to be tight. We can also cover at least 6/7 of the edges of any simple graph of maximum degree 3 by means of 3 matchings; again a tight bound.For a fixed value of a parameter k≥1, the Maximum k-Edge-Colorable Subgraph Problem asks to k-edge-color the most of the edges of a simple graph received in input. The problem is known to be APX-hard for all k≥2. However, approximation algorithms with approximation ratios tending to 1 as k goes to infinity are also known. At present, the best known performance ratios for the cases k=2 and k=3 were 5/6 and 4/5, respectively. Since the proofs of our structural result are algorithmic, we obtain an improved approximation algorithm for the case k=3, achieving approximation ratio of 6/7. Better bounds, and allowing also for parallel edges, are obtained for graphs of higher odd girth (e.g., a bound of 13/15 when the input multigraph is restricted to be triangle-free, and of 19/21 when C₅’s are also banned).     

On the approximability of the maximum induced matching problem

In this paper we consider the approximability of the maximum induced matching problem (MIM). We give an approximation algorithm with asymptotic performance ratio d−1 for MIM in d-regular graphs, for each d3. We also prove that MIM is APX-complete in d-regular graphs, for each d3.     

A 2-approximation for minmax regret problems via a mid-point scenario optimal solution

In this note, a 2-approximation method for minmax regret optimization problems is developed which extends the work of Kasperski and Zielinski [A. Kasperski, P. Zielinski, An approximation algorithm for interval data minmax regret combinatorial optimization problems, Information Processing Letters 97 (2006) 177-180] from finite to compact constraint sets.     

Efficient algorithms for decomposing graphs under degree constraints

Stiebitz [Decomposing graphs under degree constraints, J. Graph Theory 23 (1996) 321-324] proved that if every vertex v in a graph G has degree d(v)?a(v)+b(v)+1 (where a and b are arbitrarily given nonnegative integer-valued functions) then G has a nontrivial vertex partition (A,B) such that d_A(v)?a(v) for every v∈A and d_B(v)?b(v) for every v∈B. Kaneko [On decomposition of triangle-free graphs under degree constraints, J. Graph Theory 27 (1998) 7-9] and Diwan [Decomposing graphs with girth at least five under degree constraints, J. Graph Theory 33 (2000) 237-239] strengthened this result, proving that it suffices to assume d(v)?a+b (a,b?1) or just d(v)?a+b-1 (a,b?2) if G contains no cycles shorter than 4 or 5, respectively.The original proofs contain nonconstructive steps. In this paper we give polynomial-time algorithms that find such partitions. Constructive generalizations for k-partitions are also presented.     

A historical note on the 3/2-approximation algorithm for the metric traveling salesman problem

One of the most fundamental results in combinatorial optimization is the polynomial-time 3/2-approximation algorithm for the metric traveling salesman problem. It was presented by Christofides in 1976 and is well known as “the Christofides algorithm”. Recently, some authors started calling it “Christofides-Serdyukov algorithm”, pointing out that it was published independently in the USSR in 1978. We provide some historic background on Serdyukov's findings and a translation of his article from Russian into English.     

An iterative rounding 2-approximation algorithm for the k-partial vertex cover problem

We study a generalization of the vertex cover problem. For a given graph with weights on the vertices and an integer k, we aim to find a subset of the vertices with minimum total weight, so that at least k edges in the graph are covered. The problem is called the k-partial vertex cover problem. There are some 2-approximation algorithms for the problem. In the paper we do not improve on the approximation ratios of the previous algorithms, but we derive an iterative rounding algorithm. We present our technique in two algorithms. The first is an iterative rounding algorithm and gives a （2 ＋ Q/OPT ）-approximation for the k-partial vertex cover problem where Q is the largest finite weight in the problem definition and OPT is the optimal value for the instance. The second algorithm uses the first as a subroutine and achieves an approximation ratio of 2.     

A 2-approximation polynomial algorithm for a clustering problem

A 2-approximation algorithm is presented for some NP-hard data analysis problem that consists in partitioning a set of Euclidean vectors into two subsets (clusters) under the criterion of minimum sum-of-squares of distances from the elements of clusters to their centers. The center of the first cluster is the average value of vectors in the cluster, and the center of the second one is the origin.