Computational geometry through the information lens

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.Includes bibliographical references (p. 111-117).This thesis revisits classic problems in computational geometry from the modern algorithmic perspective of exploiting the bounded precision of the input. In one dimension, this viewpoint has taken over as the standard model of computation, and has led to a powerful suite of techniques that constitute a mature field of research. In two or more dimensions, we have seen great success in understanding orthogonal problems, which decompose naturally into one dimensional problems. However, problems of a nonorthogonal nature, the core of computational geometry, have remained uncracked for many years despite extensive effort. For example, Willard asked in SODA'92 for a o(nlg n) algorithm for Voronoi diagrams. Despite growing interest in the problem, it was not successfully solved until this thesis. Formally, let w be the number of bits in a computer word, and consider n points with O(w)-bit rational coordinates. This thesis describes: * a data structure for 2-d point location with O(n) space, and 0( ... )query time. * randomized algorithms with running time 9 ... ) for 3-d convex hull, 2-d Voronoi diagram, 2-d line segment intersection, and a variety of related problems. * a data structure for 2-d dynamic convex hull, with O ( ... )query time, and O ( ... ) update time. More generally, this thesis develops a suite of techniques for exploiting bounded precision in geometric problems, hopefully laying the foundations for a rejuvenated research direction.by Mihai Pǎtraşcu.S.M

Review of Elements of Parallel Computing

As the title clearly states, this book is about parallel computing. Modern computers are no longer characterized by a single, fully sequential CPU. Instead, they have one or more multicore/manycore processors. The purpose of such parallel architectures is to enable the simultaneous execution of instructions, in order to achieve faster computations. In high performance computing, clusters of parallel processors are used to achieve PFLOPS performance, which is necessary for scientific and Big Data applications. Mastering parallel computing means having deep knowledge of parallel architectures, parallel programming models, parallel algorithms, parallel design patterns, and performance analysis and optimization techniques. The design of parallel programs requires a lot of creativity, because there is no universal recipe that allows one to achieve the best possible efficiency for any problem. The book presents the fundamental concepts of parallel computing from the point of view of the algorithmic and implementation patterns. The idea is that, while the hardware keeps changing, the same principles of parallel computing are reused. The book surveys some key algorithmic structures and programming models, together with an abstract representation of the underlying hardware. Parallel programming patterns are purposely not illustrated using the formal design patterns approach, to keep an informal and friendly presentation that is suited to novices

Simple Multi-Pass Streaming Algorithms for Skyline Points and Extreme Points

In this paper, we present simple randomized multi-pass streaming algorithms for fundamental computational geometry problems of finding the skyline (maximal) points and the extreme points of the convex hull. For the skyline problem, one of our algorithm occupies O(h) space and performs O(log n) passes, where h is the number of skyline points. This improves the space bound of the currently best known result by Das Sarma, Lall, Nanongkai, and Xu [VLDB\u2709] by a logarithmic factor. For the extreme points problem, we present the first non-trivial result for any constant dimension greater than two: an O(h log^{O(1)}n) space and O(log^dn) pass algorithm, where h is the number of extreme points. Finally, we argue why randomization seems unavoidable for these problems, by proving lower bounds on the performance of deterministic algorithms for a related problem of finding maximal elements in a poset

Hardness Results for Dynamic Problems by Extensions of Fredman and Saks’ Chronogram Method

We introduce new models for dynamic computation based on the cell probe model of Fredman and Yao. We give these models access to nondeterministic queries or the right answer +-1 as an oracle. We prove that for the dynamic partial sum problem, these new powers do not help, the problem retains its lower bound of  Omega(log n/log log n). From these results we easily derive a large number of lower bounds of order Omega(log n/log log n) for conventional dynamic models like the random access machine. We prove lower bounds for dynamic algorithms for reachability in directed graphs, planarity testing, planar point location, incremental parsing, fundamental data structure problems like maintaining the majority of the prefixes of a string of bits and range queries. We characterise the complexity of maintaining the value of any symmetric function on the prefixes of a bit string