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.
3054792
Towards a theory of cache-efficient algorithms
We present a model that enables us to analyze the running time of an algorithm on a computer with a memory hierarchy with limited associativity, in terms of various cache parameters. Our cache model, an extension of Aggarwal and Vitter's I/O model, enables us to establish useful relationships between the cache complexity and the I/O complexity of computations. As a corollary, we obtain cache-efficient algorithms in the single-level cache model for fundamental problems like sorting, FFT, and an important subclass of permutations. We also analyze the average-case cache behavior of mergesort, show that ignoring associativity concerns could lead to inferior performance, and present supporting experimental evidence.We further extend our model to multiple levels of cache with limited associativity and present optimal algorithms for matrix transpose and sorting. Our techniques may be used for systematic exploitation of the memory hierarchy starting from the algorithm design stage, and for dealing with the hitherto unresolved problem of limited associativity.
33937091
Optimal, Output-Sensitive Algorithms for Constructing Upper Envelope of Line Segments in Parallel
In this paper we focus on the problem of designing very fast parallel algorithms for constructing the upper envelope of straight-line segments that achieve the O(n log H) work-bound for input size n and output size H. Our algorithms are designed for the arbitrary CRCW PRAM model. We first describe an O(log n ċ (log H + log log n)) time deterministic algorithm for the problem, that achieves O(n log H) work boundfor H = Ω(log n). We present a fast randomized algorithm that runs in expectedtime O(log H ċ log log n) with high probability and does O(n log H) work. For log H = Ω(log log n), we can achieve the running time of O(log H) while simultaneously keeping the work optimal. We also present a fast randomized algorithm that runs in O(log n/ log k) time with nk processors, k > logΩ(1) n. The algorithms do not assume any input distribution and the running times holdwith high probability.
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.
10126037
PDM sorting algorithms that take a small number of passes
We live in an era of data explosion that necessitates the discovery of novel out-of-core techniques. The I/O bottleneck has to be dealt with in developing out-of-core methods. The parallel disk model (PDM) has been proposed to alleviate the I/O bottleneck. Sorting is an important problem that has ubiquitous applications. Several asymptotically optimal PDM sorting algorithms are known and now the focus has shifted to developing algorithms for problem sizes of practical interest. In this paper we present several novel algorithms for sorting on the PDM that take only a small number of passes through the data. We also present a generalization of the zero-one principle for sorting. A shuffling lemma is presented as well. These lemmas should be of independent interest for average case analysis of sorting algorithms as well as for the analysis of randomized sorting algorithms.
29616158
Improved decremental algorithms for maintaining transitive closure and all-pairs shortest paths
This paper presents improved algorithms for the following problem: given an unweighted directed graph G(V,E) and a sequence of on-line shortest-path/reachability queries interspersed with edge-deletions, develop a data-structure that can answer each query in optimal time, and can be updated efficiently after each edge-deletion. The central idea underlying our algorithms is a scheme for implicitly storing all-pairs reachability/shortest-path information, and an efficient way to maintain this information. Our algorithms are randomized and have one-sided inverse polynomial error for query.
16079000
Searching for optimal coalition structures
Coalition formation has been a very active area of research in multiagent systems. Most of this research has concentrated on decentralized procedures that allow self-interested agents to negotiate the formation of coalitions and division of coalition payoffs. A different line of research has addressed the problem of finding the optimal division of agents into coalitions such that the sum total of the the payoffs to all the coalitions is maximized (Larson and Sandholm, 1999). This is the optimal coalition structure identification problem. Deterministic search algorithms have been proposed and evaluated under the assumption that the performance of a coalition is independent of other coalitions. We use an order-based genetic algorithm (OBGA) as a stochastic search process to identify the optimal coalition structure. We compare the performance of the OBGA with a representative deterministic algorithm presented in the literature. Though the OBGA has no performance guarantees, it is found to dominate the deterministic algorithm in a significant number of problem settings. An additional advantage of the OBGA is its scalability to larger problem sizes and to problems where performance of a coalition depends on other coalitions in the environment.
10420740
Accurate, scalable in-network identification of p2p traffic using application signatures
The ability to accurately identify the network traffic associated with different P2P applications is important to a broad range of network operations including application-specific traffic engineering, capacity planning, provisioning, service differentiation,etc. However, traditional traffic to higher-level application mapping techniques such as default server TCP or UDP network-port baseddisambiguation is highly inaccurate for some P2P applications.In this paper, we provide an efficient approach for identifying the P2P application traffic through application level signatures. We firstidentify the application level signatures by examining some available documentations, and packet-level traces. We then utilize the identified signatures to develop online filters that can efficiently and accurately track the P2P traffic even on high-speed network links.We examine the performance of our application-level identification approach using five popular P2P protocols. Our measurements show thatour technique achieves less than 5% false positive and false negative ratios in most cases. We also show that our approach only requires the examination of the very first few packets (less than 10packets) to identify a P2P connection, which makes our approach highly scalable. Our technique can significantly improve the P2P traffic volume estimates over what pure network port based approaches provide. For instance, we were able to identify 3 times as much traffic for the popular Kazaa P2P protocol, compared to the traditional port-based approach.
10902519
Effects of Local Information on Group Behavior
Researchers in the field of Distributed Artificial Intelligence have studied the effects of local decisionmaking on overall system performance in both cooperative and self-interested agent groups (Bond & Gasser, 1988) . The performance of individual agents depends critically on the quality of information available to it about local and global goals and resources. Whereas in general it is assumed that the more accurate and up-to-date the available information, the better is the expected performance of the individual and the group, this conclusion can be challenged in a number of scenarios. The populace in human societies tend to look for opportunities and search for better opportunities in their environment (Bartos, 1967) . The theory of migration in social behavior and occupational mobility suggests that the stability of the population depends on how an individual chooses its action based on the prevailing circumst antes. As agent designers, we are faced with the problem of developing decision mechanisms that allow agent societies to stabilize in states where system resources are effectively utilized. In this research, we focus on a particular aspect of distributed decision-making: the effect of limited global knowledge on group behavior. The research question that we are asking is the following: Can limited local knowledge be a boon rather than a bane in a multiagent system ? To investigate this issue we use a resource utilization problem where a number of agents are distributed between several identical resources. We assume that the cost of using any resource is directly proportional to its usage. This cost can be due to a delay in processing of the task in hand, or a reduction in the quality of the resource due to congestion. Hence, there is a justified urge in agents to seek out and move to resource with lesser usage. Other researchers have shown that such systems can exhibit oscillatory or chaotic behavior where agents move back and forth between resources (Hogg & Huberman 1991; Kephart, Hogg & Huberman 1989) resulting in ineffective uti1406 AAAI-96 lization of system resources. Whereas asynchrony, heterogeneous agent decision mechanisms, etc. has been suggested as possible means for solving the instability problem, our proposed solution of using locally differing global views is a novel mechanism to introduce asymmetry in group decisions that expedites group stability.
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.
7067374
Profit Sharing Auction
Auctions are a class of multi-party negotiation protocols. Classical auctions try to maximize social welfare by selecting the highest bidder as the winner. If bidders are rational, this ensures that the sum of profits for all bidders and the seller is maximized. In all such auctions, however, only the winner and the seller make any profit. We believe that "social welfare distribution" is a desired goal of any multiparty protocol. In the context of auctions, this goal translates into a rather radical proposal of profit sharing between all bidders and the seller. We propose a Profit Sharing Auction (PSA) where a part of the selling price paid by the winner is paid back to the bidders. The obvious criticism of this mechanism is the incentive for the seller to share its profit with nonwinning bidders. We claim that this loss can be compensated by attracting more bidders to such an auction, resulting in an associated increase in selling price. We run several sets of experiments where equivalent items are concurrently sold at a First Price Sealed Bid, a Vickrey, and a PSA auction. A population of learning bidders repeatedly choose to go to one of these auctions based on their valuation for the good being auctioned and their learned estimates of profits from these auctions. Results show that sellers make more or equivalent profits by using PSA as compared to the classical auctions. Additionally, PSA always attracts more bidders, which might create auxiliary revenue streams, and a desirable lower variability in selling prices. Interestingly then, a rational seller has the incentive to share profits and offer an auction like PSA which maximizes and distributes social welfare.