Listing all the minimum spanning trees in an undirected graph

Efficient polynomial time algorithms are well known for the minimum spanning tree problem. However, given an undirected graph with integer edge weights, minimum spanning trees may not be unique. In this article, we present an algorithm that lists all the minimum spanning trees included in the graph. The computational complexity of the algorithm is O(N(mn+n ² log n)) in time and O(m) in space, where n, m and N stand for the number of nodes, edges and minimum spanning trees, respectively. Next, we explore some properties of cut-sets, and based on these we construct an improved algorithm, which runs in O(N m log n) time and O(m) space. These algorithms are implemented in C language, and some numerical experiments are conducted for planar as well as complete graphs with random edge weights.     

Optimal search trees using two-way key comparisons

A generalization of binary search trees and binary split trees is developed that takes advantage of two-way key comparisons: the two-way comparison tree. The two-way comparison tree has little use for dynamic situations but is an improvement over the optimal binary search tree and the optimal binary split tree for static data sets. AnO(n) time and space algorithm is presented for constructing an optimal two-way comparison tree when access probabilities are equal, and an exact formula for the optimal cost is developed. The construction of the optimal two-way comparison tree for unequal access frequencies, both successful and unsuccessful, is computable inO(n ⁵) time andO(n ³) space using algorithms similar to those for the optimal binary split tree. The optimal two-way comparison tree can improve search cost by up to 50% over the optimal binary search tree.     

Algorithms for solving the symmetry number problem on trees

The symmetry number of a tree is defined as the number of nodes of the maximum subtree of the tree that exhibits axial symmetry. Chin and Yen have presented an algorithm for solving the problem of finding the symmetry number of unrooted unordered trees in time O(n⁴). In this paper we present an improved algorithm for solving the symmetry number problem on trees that runs in time O(n³). We also show that our algorithm needs O(n²logn) time for trees with bounded degrees.     

Parallel Algorithms for Maximum Matching in Complements of Interval Graphs and Related Problems

Given a set of n intervals representing an interval graph, the problem of finding a maximum matching between pairs of disjoint (nonintersecting) intervals has been considered in the sequential model. In this paper we present parallel algorithms for computing maximum cardinality matchings among pairs of disjoint intervals in interval graphs in the EREW PRAM and hypercube models. For the general case of the problem, our algorithms compute a maximum matching in O( log ³ n) time using O(n/ log ² n) processors on the EREW PRAM and using n processors on the hypercubes. For the case of proper interval graphs, our algorithm runs in O( log n ) time using O(n) processors if the input intervals are not given already sorted and using O(n/ log n ) processors otherwise, on the EREW PRAM. On n -processor hypercubes, our algorithm for the proper interval case takes O( log n log log n ) time for unsorted input and O( log n ) time for sorted input. Our parallel results also lead to optimal sequential algorithms for computing maximum matchings among disjoint intervals. In addition, we present an improved parallel algorithm for maximum matching between overlapping intervals in proper interval graphs. Received November 20, 1995; revised September 3, 1998.     

Optimal multiway search trees for variable size keys

Summary This paper considers the construction of optimal search trees for a sequence of n keys of varying sizes, under various cost measures. Constructing optimal search cost multiway trees is NP-hard, although it can be done in pseudo-polynomial time O ³ and space O ², where L is the page size limit. An optimal space multiway search tree is obtained in O ³ time and O ² space, while an optimal height tree in O(n ² log² n) time and O(n) space both having additionally minimal root sizes. The monotonicity principle does not hold for the above cases. Finding optimal search cost weak B-trees is NP-hard, but a weak B-tree of height 2 and minimal root size can be constructed in O(n log n) time. In addition, if its root is restricted to contain M keys then a different algorithm is applied, having time complexity O(nM log n). The latter solves a problem posed by McCreight.     

An Approximation Algorithm for the Minimum Co-Path Set Problem

We present an approximation algorithm for the problem of finding a minimum set of edges in a given graph G whose removal from G leaves a graph in which each connected component is a path. It achieves a ratio of \frac 107\frac {10}{7} and runs in O(n ^1.5) time, where n is the number of vertices in the input graph. The previously best approximation algorithm for this problem achieves a ratio of 2 and runs in O(n ²) time.     

Finding the convex hull of a sorted point set in parallel

We present a parallel algorithm for finding the convex hull of a sorted planar point set. Our algorithm runs in O(log n) time using O(n/log n) processors in the CREW PRAM computational model, which is optimal. One of the techniques we use to achieve these optimal bounds is the use of a parallel data structure which we call the hull tree.     

Constructing a minimum height elimination tree of a tree in linear time

Given a graph, finding an optimal vertex ranking and constructing a minimum height elimination tree are two related problems. However, an optimal vertex ranking does not by itself provide enough information to construct an elimination tree of minimum height. On the other hand, an optimal vertex ranking can readily be found directly from an elimination tree of minimum height. On n-vertex trees, the optimal vertex ranking problem already has a linear-time algorithm in the literature. However, there is no linear-time algorithm for the problem of finding a minimum height elimination tree. A naive algorithm for this problem requires O(nlogn) time. In this paper, we propose a linear-time algorithm for constructing a minimum height elimination tree of a tree.     

An optimal time and minimal space algorithm for rectangle intersection problems

Given a set ofn iso-oriented rectangles in the plane whose sides are parallel to the coordinate axes, we consider the rectangle intersection problem, i.e., finding alls intersecting pairs. The problem is well solved in the past and its solution relies heavily on unconventional data structures such as range trees, segment trees or rectangle trees. In this paper we demonstrate that classical divide-and-conquer technique and conventional data structures such as linked lists are sufficient to achieve a time bound ofO(n logn) +s, and a space bound of (n), both of which are optimal.Supported in part by the National Science Foundation under Grants MCS 8342682 and ECS 8340031.     

Optimal edge ranking of trees in polynomial time

An edge ranking of a graph is a labeling of the edges using positive integers such that all paths between two edges with the same label contain an intermediate edge with a higher label. An edge ranking isoptimal if the highest label used is as small as possible. The edge-ranking problem has applications in scheduling the manufacture of complex multipart products; it is equivalent to finding the minimum height edge-separator tree. In this paper we give the first polynomial-time algorithm to find anoptimal edge ranking of a tree, placing the problem inP. An interesting feature of the algorithm is an unusual greedy procedure that allows us to narrow an exponential search space down to a polynomial search space containing an optimal solution. AnNC algorithm is presented that finds an optimal edge ranking for trees of constant degree. We also prove that a natural decision problem emerging from our sequential algorithm isP-complete.The research of P. de la Torre was partially supported by NSF Grant CCR-9010445. R. Greenlaw's research was partially supported by NSF Grant CCR-9209184. The research of A. A. Schäffer was partially supported by NSF Grant CCR-9010534.Subsequent to the acceptance of this paper, Zhou and Nishizeki found faster algorithms for optimal edge ranking of trees, first reducing the time toO(n²) [22] and then toO(n logn) [23].     

Practical methods for constructing suffix trees

Sequence datasets are ubiquitous in modern life-science applications, and querying sequences is a common and critical operation in many of these applications. The suffix tree is a versatile data structure that can be used to evaluate a wide variety of queries on sequence datasets, including evaluating exact and approximate string matches, and finding repeat patterns. However, methods for constructing suffix trees are often very time-consuming, especially for suffix trees that are large and do not fit in the available main memory. Even when the suffix tree fits in memory, it turns out that the processor cache behavior of theoretically optimal suffix tree construction methods is poor, resulting in poor performance. Currently, there are a large number of algorithms for constructing suffix trees, but the practical tradeoffs in using these algorithms for different scenarios are not well characterized. In this paper, we explore suffix tree construction algorithms over a wide spectrum of data sources and sizes. First, we show that on modern processors, a cache-efficient algorithm with O(n²) worst-case complexity outperforms popular linear time algorithms like Ukkonen and McCreight, even for in-memory construction. For larger datasets, the disk I/O requirement quickly becomes the bottleneck in each algorithm's performance. To address this problem, we describe two approaches. First, we present a buffer management strategy for the O(n²) algorithm. The resulting new algorithm, which we call “Top Down Disk-based” (TDD), scales to sizes much larger than have been previously described in literature. This approach far outperforms the best known disk-based construction methods. Second, we present a new disk-based suffix tree construction algorithm that is based on a sort-merge paradigm, and show that for constructing very large suffix trees with very little resources, this algorithm is more efficient than TDD.     

Optimal Parallel Randomized Algorithms for the Voronoi Diagram of Line Segments in the Plane

Abstract. We present an optimal parallel randomized algorithm for the Voronoi diagram of a set of n nonintersecting (except possibly at endpoints) line segments in the plane. Our algorithm runs in O(log n) time with high probability using O(n) processors on a CRCW PRAM. This algorithm is optimal in terms of work done since the sequential time bound for this problem is Ω(n log n) . Our algorithm improves by an O(log n) factor the previously best known deterministic parallel algorithm, given by Goodrich, ó'Dúnlaing, and Yap, which runs in O( log ² n) time using O(n) processors. We obtain this result by using a new ``two-stage' random sampling technique. By choosing large samples in the first stage of the algorithm, we avoid the hurdle of problem-size ``blow-up' that is typical in recursive parallel geometric algorithms. We combine the two-stage sampling technique with efficient search and merge procedures to obtain an optimal algorithm. This technique gives an alternative optimal algorithm for the Voronoi diagram of points as well (all other optimal parallel algorithms for this problem use the transformation to three-dimensional half-space intersection).     

On Parallel Integer Merging

The problem of merging two sorted arrays A = (a₁, a₂, ..., a_n1) and B = (b₁, b₂, ..., b_n2) is considered. For input elements that are drawn from a domain of integers [1...s] we present an algorithm that runs in O(log log log s) time using n/log log log s CREW PRAM processors (optimal speed-up) and O(ns^ε) space, where n = n₁ + n₂. For input elements that are drawn from a domain of integers [1...n] we present a second algorithm that runs in O(α(n)) time (where α(n) is the inverse of Ackermann′s function) using n/α(n) CREW PRAM processors and linear space. This second algorithm is non-uniform; however, it can be made uniform at a price of a certain loss of speed, or by using a CRCW PRAM.     

Fast integer merging on the EREW PRAM

We investigate the complexity of merging sequences of small integers on the EREW PRAM. Our most surprising result is that two sorted sequences ofn bits each can be merged inO(log logn) time. More generally, we describe an algorithm to merge two sorted sequences ofn integers drawn from the set {0, ...,m?1} inO(log logn+log min{n, m}) time with an optimal time-processor product. No sublogarithmic-time merging algorithm for this model of computation was previously known. On the other hand, we show a lower bound of Ω(log min{n, m}) on the time needed to merge two sorted sequences of lengthn each with elements drawn from the set {0, ...,m?1}, implying that our merging algorithm is as fast as possible form=(logn)^Ω(1). If we impose an additional stability condition requiring the elements of each input sequence to appear in the same order in the output sequence, the time complexity of the problem becomes Θ(logn), even form=2. Stable merging is thus harder than nonstable merging.     

On Parallel Selection and Searching in Partial Orders: Sorted Matrices

Parallel algorithms for the problems of selection and searching on sorted matrices are formulated. The selection algorithm takesO(lognlog lognlog*n) time withO(n/lognlog*n) processors on an EREW PRAM. This algorithm can be generalized to solve the selection problem on a set of sorted matrices. The searching algorithm takesO(log logn) time withO(n/log logn) processors on a Common CRCW PRAM, which is optimal. We show that no algorithm using at mostnlog^cnprocessors,c≥ 1, can solve the matrix search problem in time faster than Ω(log logn) and that Ω(logn) steps are needed to solve this problem on any model that does not allow concurrent writes.     

Efficient parallel algorithms forr-dominating set andp-center problems on trees

We develop efficient parallel algorithms for ther-dominating set and thep-center problems on trees. On a concurrent-read exclusive-write PRAM, our algorithm for ther-dominating set problem runs inO(logn log logn) time withn processors. The algorithm for thep-center problem runs inO(log² n log logn) time withn processors.Xin He was supported in part by an Ohio State University Presidential Fellowship, and by the Office of Research and Graduate Studies of Ohio State University. Yaacov Yesha was supported in part by the National Science Foundation under Grant No. DCR-8606366.     

Algorithms for projecting points to give the most uniform distribution with applications to hashing

This paper presents several algorithms for projecting points so as to give the most uniform distribution. Givenn points in the plane and an integerb, the problem is to find an optimal angle ofb equally spaced parallel lines such that points are distributed most uniformly over buckets (regions bounded by two consecutive lines). An algorithm is known only in thetight case in which the two extreme lines are the supporting lines of the point set. The algorithm requiresO(bn² logn) time and On²+bn) space to find an optimal solution. In this paper we improve the algorithm both in time and space, based on duality transformation. Two linear-space algorithms are presented. One runs in On²+K log n+bn) time, whereK is the number of intersections in the transformed plane.K is shown to beO(@#@ n²+bn@#@) based on a new counting scheme. The other algorithm is advantageous ifb < n. It performs a simplex range search in each slab to enumerate all the lines that intersectbucket lines, and runs in O(b^0.610n^1.695+K logn) time. It is also shown that the problem can be solved in polynomial time even in therelaxed case. Its one-dimensional analogue is especially related to the design of an optimal hash function for a static set of keys.This work was supported in part by a Grant in Aid for Scientific Research of the Ministry of Education, Science, and Cultures of Japan.     

Parallel construction of a suffix tree with applications

Many string manipulations can be performed efficiently on suffix trees. In this paper a CRCW parallel RAM algorithm is presented that constructs the suffix tree associated with a string ofn symbols inO(logn) time withn processors. The algorithm requires Θ(n ²) space. However, the space needed can be reduced toO(n ^1+?) for any 0< ? ≤1, with a corresponding slow-down proportional to 1/?. Efficient parallel procedures are also given for some string problems that can be solved with suffix trees.     

Data-Dependency Graph Transformations for Instruction Scheduling

This paper presents a set of efficient graph transformations for local instruction scheduling. These transformations to the data-dependency graph prune redundant and inferior schedules from the solution space of the problem. Optimally scheduling the transformed problems using an enumerative scheduler is faster and the number of problems solved to optimality within a bounded time is increased. Furthermore, heuristic scheduling of the transformed problems often yields improved schedules for hard problems. The basic node-based transformation runs in O(ne) time, where n is the number of nodes and e is the number of edges in the graph. A generalized subgraph-based transformation runs in O(n² e) time. The transformations are implemented within the Gnu Compiler Collection (GCC) and are evaluated experimentally using the SPEC CPU2000 floating-point benchmarks targeted to various processor models. The results show that the transformations are fast and improve the results of both heuristic and optimal scheduling.     

An efficient all-parses systolic algorithm for general context-free parsing

The problem of outputting all parse trees of a string accepted by a context-free grammar is considered. A systolic algorithms is presented that operates inO(m·n) time, wherem is the number of distinct parse trees andn is the length of the input. The systolic array usesn ² processors, each of which requires at mostO(logn) bits of storage. This is much more space-efficient that a previously reported systolic algorithm for the same problem, which requiredO(n logn) space per processor. The algorithm also extends previous algorithms that only output a single parse tree of the input.Research squpported in part by NSF Grant DCR-8420935 and DCR-8604603.