Michael T. Goodrich

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.

P-complete geometric problems

Abstract: In this paper we show that it is impossible to solve a number of “natural” two-dimensional geometric problems in polylog time with a polynomial number of processors (unless P=NC). Thus, we disprove a popular belief that there are no natural P-complete geometric problems in the plane. The problems we address include instances of polygon triangulation, planar partitioning, and geometric layering. Our results are based on non-trivial reductions from the monotone circuit value and planar circuit value problems.

P-complete geometric problems

Abstract: In this paper we show that it is impossible to solve a number of “natural” two-dimensional geometric problems in polylog time with a polynomial number of processors (unless P=NC) Thus, we disprove a popular belief that there are no natural P-complete geometric problems in the plane The problems we address include instances of polygon triangulation, planar partitioning, and geometric layering Our results are based on non-trivial reductions from the monotone circuit value and planar circuit value problems

Fully-Dynamic Verifiable Zero-Knowledge Order Queries for Network Data.

Abstract: We show how to provide privacy-preserving (zero-knowledge) answers to order queries on network data that is organized in lists, trees, and partially-ordered sets of bounded dimension. Our methods are efficient and dynamic, in that they allow for updates in the ordering information while also providing for quick and verifiable answers to queries that reveal no information besides the answers to the queries themselves.

Linear-Time Algorithms for Geometric Graphs with Sublinearly Many Edge Crossings

Abstract: We provide linear-time algorithms for geometric graphs with sublinearly many crossings. That is, we provide algorithms running in O(n) time on connected geometric graphs having n vertices and k crossings, where k is smaller than n by an iterated logarithmic factor. Specific problems we study include Voronoi diagrams and single-source shortest paths. Our algorithms all run in linear time in the standard comparison-based computational model; hence, we make no assumptions about the distribution or bit complexities of edge weights, nor do we utilize unusual bit-level operations on memory words. Instead, our algorithms are based on a planarization method that "zeroes in" on edge crossings, together with methods for extending planar separator decompositions to geometric graphs with sublinearly many crossings. Incidentally, our planarization algorithm also solves an open computational geometry problem of Chazelle for triangulating a self-intersecting polygonal chain having n segments and k crossings in linear time, for the case when k is sublinear in n by an iterated logarithmic factor.

Generalized sweep methods for parallel computational geometry

Abstract: There are a number of algorithms in computational geometry that rely on the “sweeping” paradigm (e.g., see [15, 24, 321). The generic framework in this paradigm is for one to traverse a collection of geometric objects in some uniform way while maintaining a number of data structures for the objects that belong to a “current” set. For example, the current set of objects could be defined by all those that intersect a given vertical line as it sweeps across the plane, those that intersect a line through a point p as the line rotates around p, or those that intersect a point p as it moves through the plane. The problem is solved by updating and querying the data structures at certain stopping points, which are usually called “events”. We are interested in the problem of parallelizing sweeping algorithms. Most previous approaches to parallelizing sweeping algorithms have been to abandon the sweeping approach all together and solve the problem using a completely different paradigm. Examples include the line-segment intersection methods of Riib [36] and Goodrich [18], the

I and i

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 …

Random graphs

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.

Planning Algorithms: Introductory Material

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.

Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications

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.

An efficient k-means clustering algorithm: analysis and implementation

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.