Author
Michael T. Goodrich
Other affiliations: New York University, Technion – Israel Institute of Technology, Johns Hopkins University ...read more
Bio: Michael T. Goodrich is an academic researcher from University of California, Irvine. The author has contributed to research in topics: Planar graph & Parallel algorithm. The author has an hindex of 61, co-authored 430 publications receiving 14045 citations. Previous affiliations of Michael T. Goodrich include New York University & Technion – Israel Institute of Technology.
Papers published on a yearly basis
Papers
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08 Aug 2001
TL;DR: This paper gives a randomized online algorithm that is O(logB)-competitive against an oblivious adversary, where the bid values vary between 1 and B per item, and shows that this algorithm is optimal in the worst-case and that it performs significantly better than any worst- case bounds achievable via deterministic strategies.
Abstract: In this paper we provide an algorithmic approach to the study of online auctioning. From the perspective of the seller we formalize the auctioning problem as that of designing an algorithmic strategy that fairly maximizes the revenue earned by selling n identical items to bidders who submit bids online. We give a randomized online algorithm that is O(logB)-competitive against an oblivious adversary, where the bid values vary between 1 and B per item. We show that this algorithm is optimal in the worst-case and that it performs significantly better than any worst-case bounds achievable via deterministic strategies. Additionally we present experimental evidence to show that our algorithm outperforms conventional heuristic methods in practice. And finally we explore ways of modifying the conventional model of online algorithms to improve competitiveness of other types of auctioning scenarios while still maintaining fairness.
21 citations
29 Jun 2014
TL;DR: In this article, it was shown that it is possible to achieve a memory wear bound of log logn + O(1) after the insertion of n items into a table of size Cn for a suitable constant C using cuckoo hashing.
Abstract: We study wear-leveling techniques for cuckoo hashing, showing that it is possible to achieve a memory wear bound of loglogn + O(1) after the insertion of n items into a table of size Cn for a suitable constant C using cuckoo hashing. Moreover, we study our cuckoo hashing method empirically, showing that it significantly improves on the memory wear performance for classic cuckoo hashing and linear probing in practice.
21 citations
TL;DR: A unified framework of aesthetic criteria and complexity measures for drawing planar graphs with polylines and curves, including aspect ratio, vertex resolution, edge length, edge separation, and edge curvature is described.
21 citations
TL;DR: This work addresses the problems of finding the shortest path between two pointss andd inside P and compute the shortestpath tree between a single points and each vertex ofP, based on the divide-and-conquer paradigm.
Abstract: Given ann-vertex simple polygon we address the following problems: (i) find the shortest path between two pointss andd insideP, and (ii) compute the shortestpath tree between a single points and each vertex ofP (which implicitly represents all the shortest paths). We show how to solve the first problem inO(logn) time usingO(n) processors, and the more general second problem inO(log2n) time usingO(n) processors, and the more general second problem inO(log2n) time usingO(n) processors for any simple polygonP. We assume the CREW RAM shared memory model of computation in which concurrent reads are allowed, but no two processors should attempt to simultaneously write in the same memory location. The algorithms are based on the divide-and-conquer paradigm and are quite different from the known sequential algorithms
21 citations
Proceedings Article•
16 Jul 1990
TL;DR: An algorithm for the hidden-surface elimination problem for rectangles, which is also known as window rendering, which obtains a trade-off between these two components, in that its running time is O(r(n1+1/r+κ)), where 1≤r≤log n is a tunable parameter.
Abstract: We present an algorithm for the hidden-surface elimination problem for rectangles, which is also known as window rendering. The time complexity of our algorithm is dependent on both the number of input rectangles, n, and on the size of the output, κ. Our algorithm obtains a trade-off between these two components, in that its running time is O(r(n1+1/r+κ)), where 1≤r≤log n is a tunable parameter. By using this method while adjusting the parameter r “on the fly” one can achieve a running time that is O(n log n+κ(log n/log(1+κ/n))). Note that when κ is Θ(n), this achieves an O(n log n) running time, and when κ is Θ(n1+e) for any positive constant ɛ, then this achieves an O(κ) running time, both of which are optimal.
21 citations
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TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.
Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …
33,785 citations
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TL;DR: Some of the major results in random graphs and some of the more challenging open problems are reviewed, including those related to the WWW.
Abstract: We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.
7,116 citations
01 Jan 2006
TL;DR: This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms, into planning under differential constraints that arise when automating the motions of virtually any mechanical system.
Abstract: Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.
6,340 citations
TL;DR: An overview of the Internet of Things with emphasis on enabling technologies, protocols, and application issues, and some of the key IoT challenges presented in the recent literature are provided and a summary of related research work is provided.
Abstract: This paper provides an overview of the Internet of Things (IoT) with emphasis on enabling technologies, protocols, and application issues. The IoT is enabled by the latest developments in RFID, smart sensors, communication technologies, and Internet protocols. The basic premise is to have smart sensors collaborate directly without human involvement to deliver a new class of applications. The current revolution in Internet, mobile, and machine-to-machine (M2M) technologies can be seen as the first phase of the IoT. In the coming years, the IoT is expected to bridge diverse technologies to enable new applications by connecting physical objects together in support of intelligent decision making. This paper starts by providing a horizontal overview of the IoT. Then, we give an overview of some technical details that pertain to the IoT enabling technologies, protocols, and applications. Compared to other survey papers in the field, our objective is to provide a more thorough summary of the most relevant protocols and application issues to enable researchers and application developers to get up to speed quickly on how the different protocols fit together to deliver desired functionalities without having to go through RFCs and the standards specifications. We also provide an overview of some of the key IoT challenges presented in the recent literature and provide a summary of related research work. Moreover, we explore the relation between the IoT and other emerging technologies including big data analytics and cloud and fog computing. We also present the need for better horizontal integration among IoT services. Finally, we present detailed service use-cases to illustrate how the different protocols presented in the paper fit together to deliver desired IoT services.
6,131 citations
TL;DR: This work presents a simple and efficient implementation of Lloyd's k-means clustering algorithm, which it calls the filtering algorithm, and establishes the practical efficiency of the algorithm's running time.
Abstract: In k-means clustering, we are given a set of n data points in d-dimensional space R/sup d/ and an integer k and the problem is to determine a set of k points in Rd, called centers, so as to minimize the mean squared distance from each data point to its nearest center. A popular heuristic for k-means clustering is Lloyd's (1982) algorithm. We present a simple and efficient implementation of Lloyd's k-means clustering algorithm, which we call the filtering algorithm. This algorithm is easy to implement, requiring a kd-tree as the only major data structure. We establish the practical efficiency of the filtering algorithm in two ways. First, we present a data-sensitive analysis of the algorithm's running time, which shows that the algorithm runs faster as the separation between clusters increases. Second, we present a number of empirical studies both on synthetically generated data and on real data sets from applications in color quantization, data compression, and image segmentation.
5,288 citations