Binary search with delayed and missing answers

How many questions are necessary and sufficient to guess an unknown number x in the set S={1,2,…,n}, by using only comparison questions, that is questions of the type “Is x?a?”, a∈S, when answers to questions are received with a delay of d time units and up to c of the answers can be lost, i.e., can not be received at all? We exactly solve this problem for all integers d?0 and c=1.     

Incremental Beam search

Beam search is a heuristic search algorithm that explores a state-space graph by expanding w most promising nodes at each level (depth) of the graph, where w is called the beam-width which is taken as input from the user. The quality of the solution produced by beam search does not always monotonically improve with the increase in beam-width making it difficult to choose an appropriate beam-width for effective use. We present an algorithm called Incremental Beam Search (IncB) which guarantees monotonicity, and is also anytime in nature. Experimental results on the sliding-tile puzzle, the traveling salesman, and the single-machine scheduling problems show that IncB significantly outperforms basic monotonic methods such as iterative widening beam search as well as some of the state-of-the-art anytime heuristic search algorithms in terms of the quality of the solution produced at the end as well as the anytime performance.     

Optimal parallel algorithms for constructing and maintaining a balancedm-way search tree

We present parallel algorithms for constructing and maintaining balancedm-way search trees. These parallel algorithms have time complexity O(1) for ann processors configuration. The formal correctness of the algorithms is given in detail.     

Optimal binary search trees with costs depending on the access paths

We describe algorithms for constructing optimal binary search trees, in which the access cost of a key depends on the k preceding keys which were reached in the path to it. This problem has applications to searching on secondary memory and robotics. Two kinds of optimal trees are considered, namely optimal worst case trees and weighted average case trees. The time and space complexities of both algorithms are O(n^k+2) and O(n^k+1), respectively. The algorithms are based on a convenient decomposition and characterizations of sequences of keys which are paths of special kinds in binary search trees. Finally, using generating functions, we present an exact analysis of the number of steps performed by the algorithms.     

A simple linear time certifying LBFS-based algorithm for recognizing trivially perfect graphs and their complements

We introduce a simple, linear time algorithm for recognizing trivially perfect (TP) graphs. It improves upon the algorithm of Yan et al. [J.-H. Yan, J.-J. Chen, G.J. Chang, Quasi-threshold graphs, Discrete Appl. Math. 69 (3) (1996) 247–255] in that it is certifying, producing a P₄ or a C₄ when the graph is not TP. In addition, our algorithm can be easily modified to recognize the complement of TP graphs (co-TP) in linear time as well. It is based on lexicographic BFS, and in particular the technique of partition refinement, which has been used in the recognition of many other graph classes [D.G. Corneil, Lexicographic breadth first search—a survey, in: WG 2004, in: Lecture Notes in Comput. Sci., vol. 3353, Springer, 2004, pp. 1–19].     

A direct algorithm for restricted rotation distance

In this paper we present a concise O(n) implementation of Cleary's algorithm for generating a sequence of restricted rotations between any two binary trees. The algorithm is described directly in terms of the binary trees, without using any intermediate representation.     

An effective local search for the maximum clique problem

We propose a variable depth search based algorithm, called k-opt local search (KLS), for the maximum clique problem. KLS efficiently explores the k-opt neighborhood defined as the set of neighbors that can be obtained by a sequence of several add and drop moves that are adaptively changed in the feasible search space. Computational results on DIMACS benchmark graphs indicate that KLS is capable of finding considerably satisfactory cliques with reasonable running times in comparison with those of state-of-the-art metaheuristics.     

Efficient algorithms for clique problems

The k-clique problem is a cornerstone of NP-completeness and parametrized complexity. When k is a fixed constant, the asymptotically fastest known algorithm for finding a k-clique in an n-node graph runs in O(n0.792k) time (given by Nešet?il and Poljak). However, this algorithm is infamously inapplicable, as it relies on Coppersmith and Winograd's fast matrix multiplication.We present good combinatorial algorithms for solving k-clique problems. These algorithms do not require large constants in their runtime, they can be readily implemented in any reasonable random access model, and are very space-efficient compared to their algebraic counterparts. Our results are the following:

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We give an algorithm for k-clique that runs in O(nk/(εlogn)^k−1) time and O(nε) space, for all ε>0, on graphs with n nodes. This is the first algorithm to take o(nk) time and O(nc) space for c independent of k.

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Let k be even. Define a k-semiclique to be a k-node graph G that can be divided into two disjoint subgraphs U={u₁,…,uk/2} and V={v₁,…,vk/2} such that U and V are cliques, and for all i?j, the graph G contains the edge {ui,vj}. We give an time algorithm for determining if a graph has a k-semiclique. This yields an approximation algorithm for k-clique, in the following sense: if a given graph contains a k-clique, then our algorithm returns a subgraph with at least 3/4 of the edges in a k-clique.

     

Optimal register allocation for SSA-form programs in polynomial time

This paper gives a constructive proof that the register allocation problem for a uniform register set is solvable in polynomial time for SSA-form programs.     

Substring search and repeat search using factor oracles

We present some simple but useful properties of factor oracles, and propose fast algorithms for indexed full-text search and finding repeated substrings. Some experiments are given to demonstrate the performance of our algorithms.     

Optimal Reconstruction of Graphs under the Additive Model

We study the problem of reconstructing unknown graphs under the additive combinatorial search model. The main result concerns the reconstruction of bounded degree graphs, i.e., graphs with the degree of all vertices bounded by a constant d . We show that such graphs can be reconstructed in O(dn) nonadaptive queries, which matches the information-theoretic lower bound. The proof is based on the technique of separating matrices. A central result here is a new upper bound for a general class of separating matrices. As a particular case, we obtain a tight upper bound for the class of d -separating matrices, which settles an open question stated by Lindstr?m in [20]. Finally, we consider several particular classes of graphs. We show how an optimal nonadaptive solution of O(n ² / log n) queries for general graphs can be obtained. We also prove that trees with unbounded vertex degree can be reconstructed in a linear number of queries by a nonadaptive algorithm. Received August 1997; revised January 1999.     

QG/GA: a stochastic search for Progol

Most search techniques within ILP require the evaluation of a large number of inconsistent clauses. However, acceptable clauses typically need to be consistent, and are only found at the “fringe” of the search space. A search approach is presented, based on a novel algorithm called QG (Quick Generalization). QG carries out a random-restart stochastic bottom-up search which efficiently generates a consistent clause on the fringe of the refinement graph search without needing to explore the graph in detail. We use a Genetic Algorithm (GA) to evolve and re-combine clauses generated by QG. In this QG/GA setting, QG is used to seed a population of clauses processed by the GA. Experiments with QG/GA indicate that this approach can be more efficient than standard refinement-graph searches, while generating similar or better solutions. Editors: Ramon Otero, Simon Colton.     

A beam search approach to the container loading problem

The single container loading problem is a three-dimensional packing problem in which a container has to be filled with a set of boxes. The objective is to maximize the space utilization of the container. This problem has wide applications in the logistics industry. In this work, a new constructive approach to this problem is introduced. The approach uses a beam search strategy. This strategy can be viewed as a variant of the branch-and-bound search that only expands the most promising nodes at each level of the search tree. The approach is compared with state-of-the-art algorithms using 16 well-known sets of benchmark instances. Results show that the new approach outperforms all the others for each set of instances.     

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.     

Evolutionary local search for the edge-biconnectivity augmentation problem

This paper considers the problem of augmenting a given graph by a cheapest possible set of additional edges in order to make the graph edge-biconnected. An application is the extension of an existing communication network to become robust against single link-failures. An evolutionary algorithm (EA) is presented which includes an effective preprocessing of the problem data and a local improvement procedure that is applied during initialization, recombination, and mutation. In this way, the EA searches the space of feasible, locally optimal solutions only. The variation operators were designed with particular emphasis on low computational effort and strong locality. Empirical results indicate the superiority of the new approach over two previous heuristic methods.