Solving a system of difference constraints with variables restricted to a finite set

An algorithm is given for the solution of a system of difference constraints where the variables must take on values from a given finite set of real numbers.     

Enhanced algorithms for Local Search

Let G=(V,E) be a finite graph, and be any function. The Local Search problem consists in finding a local minimum of the function f on G, that is a vertex v such that f(v) is not larger than the value of f on the neighbors of v in G. In this note, we first prove a separation theorem slightly stronger than the one of Gilbert, Hutchinson and Tarjan for graphs of constant genus. This result allows us to enhance a previously known deterministic algorithm for Local Search with query complexity , so that we obtain a deterministic query complexity of , where n is the size of G, d is its maximum degree, and g is its genus. We also give a quantum version of our algorithm, whose query complexity is of . Our deterministic and quantum algorithms have query complexities respectively smaller than the algorithm Randomized Steepest Descent of Aldous and Quantum Steepest Descent of Aaronson for large classes of graphs, including graphs of bounded genus and planar graphs.     

A tree-covering problem arising in integrity of tree-structured data

We introduce and solve a problem motivated by integrity verification in third-party data distribution: Given an undirected tree, find a minimum-cardinality set of simple paths that cover all the tree edges and, secondarily, have smallest total path lengths. We give a linear time algorithm for this problem.     

An algorithm for partitioning trees augmented with sibling edges

We investigate a special case of the graph partitioning problem: the partitioning of a sibling graph which is an ordered tree augmented with edges connecting consecutive nodes that share a common parent. We describe the algorithm, XS, and present a proof of its correctness.     

Simplified tight analysis of Johnson's algorithm

In their paper “Tight bound on Johnson's algorithm for maximum satisfiability” [J. Comput. System Sci. 58 (3) (1999) 622-640] Chen, Friesen and Zheng provided a tight bound on the approximation ratio of Johnson's algorithm for Maximum Satisfiability [J. Comput. System Sci. 9 (3) (1974) 256-278]. We give a simplified proof of their result and investigate to what extent it may be generalized to non-Boolean domains.     

On the density and discrepancy of a 2D point set with applications to thermal analysis of VLSI chips

In this era of giga-scale integration, thermal analysis has become one of the hot topics in VLSI chip design. Active thermal sources may be abstracted as a set of weighted points on a 2D chip-floor. The conventional notion of discrepancy that deals with the congestion properties of a set of scattered points may not be able to capture properly all real-life instances in this context. In this paper, we have introduced a new concept, called the density of a region to study some of the properties of the distribution of these weighted points. We prove several counter-intuitive results concerning the properties of the regions that have maximum or minimum density. We then outline algorithms for recognizing these regions. We also compare the attributes of density with the existing concept of discrepancy.     

On Quickselect, partial sorting and Multiple Quickselect

We present explicit solutions of a class of recurrences related to the Quickselect algorithm. Thus we are immediately able to solve recurrences arising at the partial sorting problem, which are contained in this class. Furthermore we show how the partial sorting problem is connected to the Multiple Quickselect algorithm and present a method for the calculation of solutions for a class of recurrences related to the Multiple Quickselect algorithm. Further an analysis of an algorithm for sorting a subarray A[r…r+p−1], given the array A[1…n], is provided.     

A note on maximum independent set and related problems on box graphs

A box graph is the intersection graph of orthogonal rectangles in the plane. We show that maximum independent set and minimum vertex cover on box graphs can be solved in subexponential time, more precisely, in time , by applying Miller's simple cycle planar separator theorem [J. Comput. System Sci. 32 (1986) 265-279] (in spite of the fact that the input box graph might be strongly non-planar).     

Success probability of the Hellman trade-off

Cryptanalytic time memory trade-off is a probabilistic algorithm for inverting a generic one-way function. Since its first introduction by Hellman, many variants and their analysis results have appeared. We present a new estimate for the success probability of the original Hellman trade-off, that is more accurate than the lower bound that is widely being used today.     

On the All-Farthest-Segments problem for a planar set of points

In this note, we outline a very simple algorithm for the following problem: Given a set S of n points p₁,p₂,p₃,…,pn in the plane, we have O(n²) segments implicitly defined on pairs of these n points. For each point pi, find a segment from this set of implicitly defined segments that is farthest from pi. The time complexity of our algorithm is in O(nh+nlogn), where n is the number of input points, and h is the number of vertices on the convex hull of S.     

Using spine decompositions to efficiently solve the length-constrained heaviest path problem for trees

The length-constrained heaviest path (LCHP) in a weighted tree T, where each edge is assigned a weight and a length, is the path P in T with maximum total path weight and total path length bounded by a given value B. This paper presents an O(nlogn) time LCHP algorithm which utilizes a data structure constructed from the spine decomposition of the input tree. This is an improvement over the existing algorithm by Wu et al. (1999), which runs in O(nlog²n) time. Our method also improves on a previous O(nlogn) time algorithm by Kim (2005) for the special case of finding a longest nonnegative path in a constant degree tree in that we can handle trees of arbitrary degree within the same time bounds.     

Minimal on-line labelling

The problem of on-line labelling is one of assigning integer labels in the range 1 to N to an input stream of at most N distinct items, drawn from a linearly ordered set, so that at each step the labels respect the ordering on the items. To maintain this constraint, items may have to be relabelled to accommodate new ones. With T(M,N) denoting the total number of relabellings that have to be performed for the first M inputs, it is known that for any given constant c in the range 0<c<1 there are exact bounds T(Nc,N)=Θ(NlogN) and T(cN,N)=Θ(Nlog²N). However, in the case c=1, when the labelling is called minimal, is known only that T(N,N)=O(Nlog³N). Existing algorithms for minimal on-line labelling are complicated, and our aim in this paper is to give a simplified and self-contained account of the problem.     

Double hashing with passbits

Double hashing with bucket capacity one is augmented with multiple passbits to obtain significant reduction to unsuccessful search lengths. This improves the analysis of Martini et al. [P.M. Martini, W.A. Burkhard, Double hashing with multiple passbits, Internat. J. Found. Theoret. Comput. Sci. 14 (6) (2003) 1165-1188] by providing a closed form expression for the expected unsuccessful search lengths.     

A variant of the Ford-Johnson algorithm that is more space efficient

A variant of the Ford-Johnson or merge insertion sorting algorithm that we called four Ford-Johnson (₄FJ, for short) is presented and proved to execute exactly the same number of comparisons than the Ford-Johnson algorithm. The main advantage of our algorithm is that, instead of recursively working over lists of size the half of the input, as the Ford-Johnson algorithm does, ₄FJ recursively works over lists of size the quarter of the input. This allows for implementations of data structures for coordinating the recursive calls of size only 33% of the ones needed for the Ford-Johnson algorithm.     

A note on compressed sensing and the complexity of matrix multiplication

We consider the conjectured O(N2+?) time complexity of multiplying any two N×N matrices A and B. Our main result is a deterministic Compressed Sensing (CS) algorithm that both rapidly and accurately computes A⋅B provided that the resulting matrix product is sparse/compressible. As a consequence of our main result we increase the class of matrices A, for any given N×N matrix B, which allows the exact computation of A⋅B to be carried out using the conjectured O(N2+?) operations. Additionally, in the process of developing our matrix multiplication procedure, we present a modified version of Indyk's recently proposed extractor-based CS algorithm [P. Indyk, Explicit constructions for compressed sensing of sparse signals, in: SODA, 2008] which is resilient to noise.     

Trading uninitialized space for time

The design of efficient graph algorithms usually precludes the test of edge existence, because an efficient support of that operation already requires time for the initialization of an adjacency-matrix representation. We describe an alternative representation of static directed graphs taking Θ(n+m) initialization time and using Θ(n²) space, which supports the efficient implementation of all usual operations on static graphs. The sparse graph representation allows the design of efficient graph algorithms using both iteration over all vertices adjacent with a given vertex and edge-existence operations, although at the expense of additional (uninitialized) space which may, nevertheless, be used for other purposes. To the best of our knowledge, the representation leads to the first graph algorithms with the disconcerting property that the time complexity is better than the space complexity.     

On mimicking networks representing minimum terminal cuts

Given a capacitated undirected graph G=(V,E)$G=(V,E)$ with a set of terminals K⊂V$K\subset V$, a mimicking network   is a smaller graph H=(_VH,_EH)$H=(V_H,E_H)$ which contains the set of terminals K   and for every bipartition [U,K−U]$[U,K-U]$ of the terminals, the cost of the minimum cut separating U   from K−U$K-U$ in G is exactly equal to the cost of the minimum cut separating U   from K−U$K-U$ in H.     

Optimal algorithm for a special point-labeling problem

A special class of map labeling problem is studied. Let P={p₁,p₂,…,p_n} be a set of point sites distributed on a 2D map. A label associated with each point p_i is an axis-parallel rectangle r_i of specified width. The height of all are same. The placement of r_i must contain p_i at its top-left or bottom-left corner, and it does not obscure any other point in P. The objective is to label the maximum number of points on the map so that the placed labels are mutually non-overlapping. We first consider a simple model for this problem. Here, for each point p_i, the corner specification (i.e., whether the point p_i would appear at the top-left or bottom-left corner of the label) is known a priori. We show that the time complexity of this problem is , and then propose an algorithm for this problem which runs in O(nlogn) time. If the corner specifications of the points in P are not known, our algorithm is a 2-approximation algorithm. Here we propose an efficient heuristic algorithm that is easy to implement. Experimental evidences show that it produces optimal solutions for most of the randomly generated instances and for all the standard benchmarks available in http://www.math-inf.uni-greifswald.de/map-labeling/.