In this paper we discuss approximation algorithms for the ELEMENT-DISJOINT STEINER TREE PACKING problem (Element-STP for short). For a graph G=(V,E) and a subset of nodes T⊆V, called terminal nodes, a Steiner tree is a connected, acyclic subgraph that contains all the terminal nodes in T. The goal of Element-STP is to find as many element-disjoint Steiner trees as possible. Element-STP is known to be APX-hard even for |T|=3 [1]. It is also known that Element-STP is NP-hard to approximate within a factor of Ω(log |V|) [3] and there is an O(log |V|)-approximation algorithm for Element-STP [2],[4]. In this paper, we provide a $lceil rac{|T|}{2} ceil$-approximation algorithm for Element-STP on graphs with |T| terminal nodes. Furthermore, we show that the approximation ratio of 3 for Element-STP on graphs with five terminal nodes can be improved to 2.

A vertex cover of a given graph G = (V,E) is a subset N of V such that N contains either u or v for any edge (u,v) of E. The minimum weight vertex cover problem (MWVC for short) is the problem of finding a vertex cover N of any given graph G = (V,E), with weight w(v) for each vertex v of V, such that the sum w(N) of w(v) over all v of N is minimum. In this paper, we consider MWVC with w(v) of any v of V being a positive integer. We propose simple procedures as postprocessing of algorithms for MWVC. Furthremore, five existing approximation algorithms with/without the proposed procedures incorporated are implemented, and they are evaluated through computing experiment.

In this paper, we consider a problem of simultaneously optimizing a sequence of graphs and a route which exhaustively visits the vertices from each pair of successive graphs in the sequence. This type of problem arises from repetitive routing of grasp-and-delivery robots used in the production of printed circuit boards. The problem is formulated as follows. We are given a metric graph G*=(V*,E*), a set of m+1 disjoint subsets Ci ⊆ V* of vertices with |Ci|=n, i=0,1,...,m, and a starting vertex s ∈ C0. We seek to find a sequence π=(Ci1, Ci2, ..., Cim) of the subsets of vertices and a shortest walk P which visits all (m+1)n vertices in G* in such a way that after starting from s, the walk alternately visits the vertices in Cik-1 and Cik, for k=1,2,...,m (i0=0). Thus, P is a walk with m(2n-1) edges obtained by concatenating m alternating Hamiltonian paths between Cik-1 and Cik, k=1,2,...,m. In this paper, we show that an approximate sequence of subsets of vertices and an approximate walk with at most three times the optimal route length can be found in polynomial time.

It is well known that a basic version (i.e., maximizing the number of base-pairs) of the RNA secondary structure prediction problem can be solved in O(n3) time by using simple dynamic programming procedures. For this problem, an O(n3(log log n)1/2/(log n)1/2) time exact algorithm and an O(n2.776+(1/ε)O(1)) time approximation algorithm which has guaranteed approximation ratio 1-ε for any positive constant ε are also known. Moreover, when two RNA sequences are given, there is an O(n6) time exact algorithm which can optimize structure and alignments. In this paper, we show an O(n5) time approximation algorithm for optimizing structure and alignments of two RNA sequences with assuming that the optimal number of base-pairs is more than O(n0.75). We also show that the problem to optimize structure and alignments for given N sequences is NP-hard and introduce a constant-factor approximation algorithm.

In this paper we study the problem of how to identify multiple disjoint paths that have the minimum total cost OPT and satisfy a delay bound D in a graph G. This problem has lots of applications in networking such as fault-tolerant quality of service (QoS) routing and network-flow load balancing. Recently, several approximation algorithms have been developed for this problem. Here, we propose a new approximation algorithm for it by using the Lagrangian Relaxation method. We then present a simple approximation algorithm for finding multiple link-disjoint paths that satisfy the delay constraints at a reasonable total cost. If the optimal solution under delay-bound D has a cost OPT, then our algorithm can find a solution whose delay is bounded by (1+)D and the cost is no more than (1+k)OPT. The time complexity of our algorithm is much better than the previous algorithms.

An instance of the classical stable marriage problem requires all participants to submit a strictly ordered preference list containing all members of the opposite sex. However, considering applications in real-world, we can think of two natural relaxations, namely, incomplete preference lists and ties in the lists. Either variation leaves the problem polynomially solvable, but it is known that finding a maximum cardinality stable matching is NP-hard when both variations are allowed. It is easy to see that the size of any two stable matchings differ by at most a factor of two, and so, an approximation algorithm with a factor two is trivial. A few approximation algorithms have been proposed with approximation ratio better than two, but they are only for restricted instances, such as restricting occurrence of ties and/or lengths of ties. Up to the present, there is no known approximation algorithm with ratio better than two for general inputs. In this paper, we give the first nontrivial result for approximation of factor less than two for general instances. Our algorithm achieves the ratio for an arbitrarily positive constant c, where N denotes the number of men in an input.

The (k + δ)-edge-connectivity augmentation problem for a specified set of vertices ((k + δ)ECA-SV) is defined as follows: "Given an undirected graph G =(V,E), a specified set of vertices Γ V, a subgraph G ′=(V,E ′) with λ(Γ;G ′) = k of G and a cost function c: E Z+ (nonnegative integers), find a set E* E - E ′of edges, each connecting distinct vertices of V, of minimum total cost such that λ(Γ;G″) k + δ for G"=(V,E ′∪E*)," where λ(Γ;G″) is the minimum value of the maximum number of edge disjoint paths between any pair of vertices in Γ of G". The paper proposes an O(Δ+|V||E|) time 2-approximation algorithm FSAR for (k + 1)ECA-SV with a restriction λ(V;G ′) = λ(Γ;G ′), where Δ is the time complexity of constructing a structural graph of a given graph G ′.

Karger, Motwani and Sudan presented a graph coloring algorithm based on semidefinite programming, which colors any k-colorable graph with maximum degree Δ using (Δ1-2/k) colors. This algorithm leads to an algorithm for k-colorable graph using (n 1-3/(k+1)) colors. This improved Wigderson's algorithm, which uses O(n1-1/(k-1)) colors, containing as a subroutine an algorithm using (Δ+1) colors for graphs with maximum degree Δ. It is easy to imagine that an algorithm which uses less colors in terms of Δ leads to an algorithm which uses less colors in terms of n. In this paper, we consider this influence assuming that we have an algorithm which uses (Δ 1-x/k) colors for 2

The minimum distance of a linear code C is a useful metric property for estimating the error correction upper bound of C and the maximum likelihood decoding of a linear code C is also of practical importance and of theoretical interest. These problems are known to be NP-hard to approximate within any constant relative error to the optimum. As a problem related to the above, we consider the maximization problem MAX-WEIGHT: Given a generator matrix of a linear code C, find a codeword c C with its weight as close to the maximum weight of C as possible. It is shown that MAX-WEIGHT PTAS unless P=NP, however, no nontrivial approximation upper and lower bounds are known. In this paper, we investigate the complexity of MAX-WEIGHT to make the approximation upper and lower bounds more precise, and show that (1) MAX-WEIGHT is APX-complete; (2) MAX-WEIGHT is approximable within relative error 1/2 to the optimum; (3) MAX-WEIGHT is not approximable within relative error 1/10 to the optimum unless P=NP.

The main problem considered is submodular set cover, the problem of minimizing a linear function under a nondecreasing submodular constraint, which generalizes both well-known set cover and minimum matroid base problems. The problem is NP-hard, and two natural greedy heuristics are introduced along with analysis of their performance. As applications of these heuristics we consider various special cases of submodular set cover, including partial cover variants of set cover and vertex cover, and node-deletion problems for hereditary and matroidal properties. An approximation bound derived for each of them is either matching or generalizing the best existing bounds.

Maximum Satisfiability Problem (MAX SAT) is one of the most natural optimization problems. Since it is known to be NP-hard, approximation algorithms have been considered. The aim of this survey is to show recent developments of approximation algorithms for MAX SAT.

The subject of the paper is the minimum initial marking problem for scheduling in timed Petri net PN: given a vector X of nonnegative integers, a P-invariant Y of PN and a nonnegative integer π, find an initial marking M minimizing the value YtrM among those initial marking M such that there is a scheduling σ having the total completion time τ(σ)π with respect M , X and PN (a sequence of transitions, with the first transition firable on M , such that every transition t can fire prescribed number X(t) of times). The paper shows NP-hardness of the problem and proposes two approximation algorithms with their experimental evaluation.

The subject of the paper is to propose two approximation algorithms FM_SPLA, FM_DPLA for priority-list scheduling in timed Petri nets. Their capability is compared with that of existing algorithms SPLA, DPLA through experimental results, where SPLA and DPLA have previously been proposed by the authors.