31766769
Improved selection in totally monotone arrays
This paper's main result is an -time algorithm for computing the kth smallest entry in each row of an m×n totally monotone array. (A two-dimensional array A={a[i, j]} is totally monotone if for all i1<i2 and j1<j2, a[i1, j1]<a[i1, j2] implies a[i2, j1]<a[i2, j2].) For large values of k (in particular, for k=⌈n/2⌉), this algorithm is significantly faster than the O(k(m + n))-time algorithm for the same problem due to Kravets and Park. An immediate consequence of this result is an O(n3/2lg1/2 n)-time algorithm for computing the kth nearest neighbor of each vertex of a convex n-gon. In addition to the main result, we also give an O(n lg m)-time algorithm for computing an approximate median in each row of an m×n totally monotone array; this approximate median is an entry whose rank in its row lies between ⌊n/4⌋ and ⌈3n/4⌉ − 1.
32334601
Finding an Approximate Median with High Probability in Constant Parallel Time
Given n elements from a totally ordered set we outline a method for choosing an element whose rank is γn for some 0 0.
9302549
Planar Graph Blocking for External Searching

AbstractWe present a new scheme for storing a planar graph in external memory so that any online path can be traversed in an I-O efficient way. Our storage scheme significantly improves the previous results for planar graphs with bounded face size. We also prove an upper bound on I-O efficiency of any storage scheme for well-shaped triangulated meshes. For these meshes, our storage scheme achieves optimal performance.

10562059
Improved decremental algorithms for maintaining transitive closure and all-pairs shortest paths
We present improved algorithms for maintaining transitive closure and all-pairs shortest paths/distances in a digraph under deletion of edges.(MATH) For the problem of transitive closure, the previous best known algorithms, for achieving O(1) query time, require O(\min(m, \frac{n^3}{m}))$ amortized update time, implying an upper bound of O(n^{\frac{3}{2}})$ on update time per edge-deletion. We present an algorithm that achieves $O(1)$ query time and O(n \log^2n + \frac{n^2}{\sqrt{m}}{\sqrt{\log n}})$ update time per edge-deletion, thus improving the upper bound to O(n^{\frac{4}{3}}\sqrt[3]{\log n})$.(MATH) For the problem of maintaining all-pairs shortest distances in unweighted digraph under deletion of edges, we present an algorithm that requires O(\frac{n^3}{m} \log^2 n)$ amortized update time and answers a distance query in O(1) time. This improves the previous best known update bound by a factor of log n. For maintaining all-pairs shortest paths, we present an algorithm that achieves O(\min(n^{\frac{3}{2}} \sqrt{\log n}, \frac{n^3}{m} \log ^2n))$ amortized update time and reports a shortest path in optimal time (proportional to the length of the path). For the latter problem we improve the worst amortized update time bound by a factor of O(\sqrt{\frac{n}{\log n}})$.(MATH) We also present the first decremental algorithm for maintaining all-pairs (1+ε) approximate shortest paths/distances, for any ε > 0, that achieves a sub-quadratic update time of O(n log2n + \frac{n^2}{\sqrt{\epsilon m}}\sqrt{\log n})$ and optimal query time.Our algorithms are randomized and have one-sided error for query (with probability O(1/nc) for any constant c).
1068367
An Efficient Output-Size Sensitive Parallel Algorithm for Hidden-Surface Removal for Terrains
Abstract. We describe an efficient parallel algorithm for hidden-surface removal for terrain maps. The algorithm runs in O(log 4 n) steps on the CREW PRAM model with a work bound of O((n+k) \polylog ( n)) where n and k are the input and output sizes, respectively. In order to achieve the work bound we use a number of techniques, among which our use of persistent data structures is somewhat novel in the context of parallel algorithms.
11169442
Linear Time Algorithms for Clustering Problems in Any Dimensions
We generalize the k-means algorithm presented by the authors [14] and show that the resulting algorithm can solve a larger class of clustering problems that satisfy certain properties (existence of a random sampling procedure and tightness). We prove these properties for the k-median and the discrete k-means clustering problems, resulting in O(2(k/e)O(1)dn) time (1+e)-approximation algorithms for these problems. These are the first algorithms for these problems linear in the size of the input (nd for n points in d dimensions), independent of dimensions in the exponent, assuming k and e to be fixed. A key ingredient of the k-median result is a (1+e)-approximation algorithm for the 1-median problem which has running time O(2(1/e)O(1)d). The previous best known algorithm for this problem had linear running time.
13878075
Optimal sequencing of individually rational contracts
Multiagent researchers have worked on the problem of determining optimal contracts between self-interested agents. In particular, Sandholm et al. [1,8] have both theoretically and experimentally studied the necessity and usefulness of different contract types under the assumption of individually myopically rational contracting. We study a variant of sequential contracting where the goal is to maximize social welfare through a fixed-length sequence of individually rational contracts. The space of possible contract sequences is exponential. We compare a greedy deterministic heuristic with a stochastic genetic algorithm based approach for this optimal sequential contract selection problem. We focus on sub-additive domains where individually rational contracts are feasible with side payments. We show that the GA-based approach consistently outperforms the deterministic heuristic by generating larger social welfare.
16723670
PLEASE: A Prototype Learning System Using Genetic Algorithms
Prototypes have been proposed as representation of concepts that are used eeec-tively by humans. Developing computational schemes for generating prototypes from examples , however, has proved to be a dii-cult problem. We present a novel genetic algorithm based prototype learning system, PLEASE, for constructing appropriate prototypes from classiied training instances. After constructing a set of prototypes for each of the possible classes, the class of a new input instance is determined by the nearest prototype to this instance. Attributes are assumed to be ordinal in nature and prototypes are represented as sets of feature-value pairs. A genetic algorithm is used to evolve the number of prototypes per class and their positions on the input space. We present experimental results on a series of artiicial problems of varying complexity. PLEASE performs competitively with several nearest neighbor clas-siication algorithms on the problem set. An analysis of the strengths and weaknesses of the initial version of our system motivates the need for additional operators. The inclusion of these operators substantially improves the performance of the system on particularly diicult problems.
2225573
Integrated scheduling of multimedia and hard real-time tasks
An integrated platform which is capable of meeting the requirements of both traditional real-time control processing and multimedia processing has enormous potential for accommodating various kinds of new applications. However, except for the simplest of situations, few, if any, research or commercial systems successfully provide architectural and OS mechanisms which can efficiently support both hard real-time computation and multimedia soft real-time computation. The authors propose a multimedia server execution on multiprocessor real-time operating systems to provide different classes of guarantee to support both types of processing. The multimedia server supports multiple periodic multimedia streams with a capability for graceful QoS degradation during system overload. They (i) develop several multimedia server scheduling algorithms, (ii) evaluate the performance of these algorithms, and (iii) discuss realistic system implementation issues on the SGI IRIX/REACT/PRO operating system.
2727968
A bayes net approach to argumentation
Argumentation-based negotiation approaches have been proposed to capture realistic negotiation contexts where all issues being negotiated are not fixed a priori and the negotiatorýs beliefs may be influenced during the negotiation process. We have proposed a novel Bayesian network based argumentation and decision making framework that allows agents to utilize models of other agents. We discuss a decision mechanism for generating the most effective argument to convince the opponent to accept favorable contracts.
7601656
Effect of referrals on convergence to satisficing distributions
We investigate a framework where agents locate high-quality service providers by using referrals from peer agents. The performance of providers is measured by the satisfaction obtained by agents from using their services. Provider performance depends upon its intrinsic capability and upon its current load. We present an algorithm for selecting a service provider for a given task which includes mechanisms for deciding when and who to ask for a referral. This mechanism requires learning, over interactions, both the performance levels of different service providers, as well as the quality of referrals provided by other agents. We use a satisficing rather than an optimizing framework, where agents are content to receive service quality above a threshold. Agents have to learn the quality of others' referrals and the quality of providers to find satisficing providers. We compare the effectiveness of referral systems with or without deception with systems without referrals. We identify zones, based on an observed entropy metric, where using referrals is helpful in promoting fast convergence to satisficing distributions.