Augmented k-ary n-cubes

We define an interconnection network AQ_n,k which we call the augmented k-ary n-cube by extending a k-ary n-cube in a manner analogous to the existing extension of an n-dimensional hypercube to an n-dimensional augmented cube. We prove that the augmented k-ary n-cube AQ_n,k has a number of attractive properties (in the context of parallel computing). For example, we show that the augmented k-ary n-cube AQ_n,k: is a Cayley graph, and so is vertex-symmetric, but not edge-symmetric unless n = 2; has connectivity 4n − 2 and wide-diameter at most max{(n − 1)k − (n − 2), k + 7}; has diameter , when n = 2; and has diameter at most , for n ? 3 and k even, and at most , for n ? 3 and k odd.     

The interval-merging problem

A closed interval is an ordered pair of real numbers [x, y], with x ? y. The interval [x, y] represents the set {i ∈ R∣x ? i ? y}. Given a set of closed intervals I={[a₁,b₁],[a₂,b₂],…,[a_k,b_k]}, the Interval-Merging Problem is to find a minimum-cardinality set of intervals M(I)={[x₁,y₁],[x₂,y₂],…,[x_j,y_j]}, j ? k, such that the real numbers represented by equal those represented by . In this paper, we show the problem can be solved in O(d log d) sequential time, and in O(log d) parallel time using O(d) processors on an EREW PRAM, where d is the number of the endpoints of I. Moreover, if the input is given as a set of sorted endpoints, then the problem can be solved in O(d) sequential time, and in O(log d) parallel time using O(d/log d) processors on an EREW PRAM.     

A dynamic programming algorithm for simulation of a multi-dimensional torus in a crossed cube

The torus is a popular interconnection topology and several commercial multicomputers use a torus as the basis of their communication network. Moreover, there are many parallel algorithms with torus-structured and mesh-structured task graphs have been developed. If one network can embed a mesh or torus network, the algorithms with mesh-structured or torus-structured can also be used in this network. Thus, the problem of embedding meshes or tori into networks is meaningful for parallel computing. In this paper, we prove that for n ? 6 and 1 ? m ? ⌈n/2⌉ − 1, a family of 2^m disjoint k-dimensional tori of size 2^s₁×2^s₂×?×2^s_k each can be embedded in an n-dimensional crossed cube with unit dilation, where each s_i ? 2, , and max_1?i?k{s_i} ? 3 if n is odd and ; otherwise, max_1?i?k{s_i} ? n − 2m − 1. A new concept, cycle skeleton, is proposed to construct a dynamic programming algorithm for embedding a desired torus into the crossed cube. Furthermore, the time complexity of the algorithm is linear with respect to the size of desired torus. As a consequence, a family of disjoint tori can be simulated on the same crossed cube efficiently and in parallel.     

How much precision is needed to compare two sums of square roots of integers?

In this paper, we investigate the problem of the minimum nonzero difference between two sums of square roots of integers. Let r(n,k) be the minimum positive value of where ai and bi are integers not larger than integer n. We prove by an explicit construction that r(n,k)=O(n−2k+3/2) for fixed k and any n. Our result implies that in order to compare two sums of k square roots of integers with at most d digits per integer, one might need precision of as many as digits. We also prove that this bound is optimal for a wide range of integers, i.e., r(n,k)=Θ(n−2k+3/2) for fixed k and for those integers in the form of and , where n^′ is any integer satisfied the form and i is any integer in [0,k−1]. We finally show that for k=2 and any n, this bound is also optimal, i.e., r(n,2)=Θ(n−7/2).     

A combinatorial algorithm for max csp

We consider the problem max csp over multi-valued domains with variables ranging over sets of size s_i?s and constraints involving k_j?k variables. We study two algorithms with approximation ratios A and B, respectively, so we obtain a solution with approximation ratio max(A,B).The first algorithm is based on the linear programming algorithm of Serna, Trevisan, and Xhafa [Proc. 15th Annual Symp. on Theoret. Aspects of Comput. Sci., 1998, pp. 488-498] and gives ratio A which is bounded below by s^1−k. For k=2, our bound in terms of the individual set sizes is the minimum over all constraints involving two variables of , where s₁ and s₂ are the set sizes for the two variables.We then give a simple combinatorial algorithm which has approximation ratio B, with B>A/e. The bound is greater than s^1−k/e in general, and greater than s^1−k(1−(s−1)/2(k−1)) for s?k−1, thus close to the s^1−k linear programming bound for large k. For k=2, the bound is if s=2, 1/2(s−1) if s?3, and in general greater than the minimum of 1/4s₁+1/4s₂ over constraints with set sizes s₁ and s₂, thus within a factor of two of the linear programming bound.For the case of k=2 and s=2 we prove an integrality gap of . This shows that our analysis is tight for any method that uses the linear programming upper bound.     

On the Euler sequence spaces which include the spaces ?p and ?∞ I

In the present paper, we introduce the Euler sequence space consisting of all sequences whose Euler transforms of order r are in the space ?_p of non-absolute type which is the BK-space including the space ?_p and prove that the spaces and ?_p are linearly isomorphic for 1 ? p ? ∞. Furthermore, we give some inclusion relations concerning the space . Finally, we determine the α-, β- and γ-duals of the space for 1 ? p ? ∞ and construct the basis for the space , where 1 ? p < ∞.     

Optimal algorithms for the average-constrained maximum-sum segment problem

Given a real number sequence A=(a₁,a₂,…,an), an average lower bound L, and an average upper bound U, the Average-Constrained Maximum-Sum Segment problem is to locate a segment A(i,j)=(ai,ai+1,…,aj) that maximizes _∑i?k?jak subject to . In this paper, we give an O(n)-time algorithm for the case where the average upper bound is ineffective, i.e., U=∞. On the other hand, we prove that the time complexity of the problem with an effective average upper bound is Ω(nlogn) even if the average lower bound is ineffective, i.e., L=−∞.     

The k-fundamental group of a closed k-surface

In this paper, we deal with the problem of computing the digital fundamental group of a closed k-surface by using various properties of both a (simple) closed k-surface and a digital covering map. To be specific, let be a simple closed k_i-curve with l_i elements in Z^n_i, i∈{1,2}. Then, the Cartesian product is not always a closed k-surface with some k-adjacency of Z^n₁+n₂. Thus, we provide a condition for to be a (simple) closed k-surface with some k-adjacency depending on the k_i-adjacency, i∈{1,2}. Besides, even if is not a closed k-surface, we show that the k-fundamental group of can be calculated by both a k-homotopic thinning and a strong k-deformation retract.     

Acyclic and k-distance coloring of the grid

In this paper, we give a relatively simple though very efficient way to color the d-dimensional grid G(n₁,n₂,…,n_d) (with n_i vertices in each dimension 1?i?d), for two different types of vertex colorings: (1) acyclic coloring of graphs, in which we color the vertices such that (i) no two neighbors are assigned the same color and (ii) for any two colors i and j, the subgraph induced by the vertices colored i or j is acyclic; and (2) k-distance coloring of graphs, in which every vertex must be colored in such a way that two vertices lying at distance less than or equal to k must be assigned different colors. The minimum number of colors needed to acyclically color (respectively k-distance color) a graph G is called acyclic chromatic number of G (respectively k-distance chromatic number), and denoted a(G) (respectively χ_k(G)).The method we propose for coloring the d-dimensional grid in those two variants relies on the representation of the vertices of G_d(n₁,…,n_d) thanks to its coordinates in each dimension; this gives us upper bounds on a(G_d(n₁,…,n_d)) and χ_k(G_d(n₁,…,n_d)).We also give lower bounds on a(G_d(n₁,…,n_d)) and χ_k(G_d(n₁,…,n_d)). In particular, we give a lower bound on a(G) for any graph G; surprisingly, as far as we know this result was never mentioned before. Applied to the d-dimensional grid G_d(n₁,…,n_d), the lower and upper bounds for a(G_d(n₁,…,n_d)) match (and thus give an optimal result) when the lengths in each dimension are “sufficiently large” (more precisely, if ). If this is not the case, then these bounds differ by an additive constant at most equal to . Concerning χ_k(G_d(n₁,…,n_d)), we give exact results on its value for (1) k=2 and any d?1, and (2) d=2 and any k?1.In the case of acyclic coloring, we also apply our results to hypercubes of dimension d, H_d, which are a particular case of G_d(n₁,…,n_d) in which there are only 2 vertices in each dimension. In that case, the bounds we obtain differ by a multiplicative constant equal to 2.     

Embedding a family of disjoint multi-dimensional meshes into a crossed cube

Crossed cubes are an important class of hypercube variants. This paper addresses how to embed a family of disjoint multi-dimensional meshes into a crossed cube. We prove that for n?4 and 1?m?⌊n/2⌋−1, a family of m² disjoint k-dimensional meshes of size t₁²×t₂²×?×tk² each can be embedded in an n-dimensional crossed cube with unit dilation, where and max1?i?k{ti}?n−2m−1. This result means that a family of mesh-structured parallel algorithms can be executed on a same crossed cube efficiently and in parallel. Our work extends some recently obtained results.