Robert E. Tarjan

Bio: Robert E. Tarjan is an academic researcher from Princeton University. The author has contributed to research in topics: Time complexity & Spanning tree. The author has an hindex of 114, co-authored 400 publications receiving 67305 citations. Previous affiliations of Robert E. Tarjan include AT&T & Massachusetts Institute of Technology.

Faster and simpler algorithm for sorting signed permutations by reversals

Abstract: We give a quadratic algorithm for finding the minimum number of reversals needed to sort a signed permutation. Our algorithm is faster than the previous algorithm of Hannenhalli and Pevzner and its faster implementation of Berman and Hannenhalli. The algorithm is conceptually simple and does not require special data structures. Our study also considerably simplifies the combinatorial structures used by the analysis. 2 refs.

Simple Confluently Persistent Catenable Lists

Abstract: We consider the problem of maintaining persistent lists subject to concatenation and to insertions and deletions at both ends. Updates to a persistent data structure are nondestructive---each operation produces a new list incorporating the change, while keeping intact the list or lists to which it applies. Although general techniques exist for making data structures persistent, these techniques fail for structures that are subject to operations, such as catenation, that combine two or more versions. In this paper we develop a simple implementation of persistent double-ended queues (deques) with catenation that supports all deque operations in constant amortized time. Our implementation is functional if we allow memoization.

b-Matchings in Trees

Abstract: We develop linear-time algorithms to find maximum weighted and unweighted degree-constrained subgraphs (b-matchings) of a tree. We use a generalization of an algorithm for finding a maximum 2-matching in a tree.

Finding the Triconnected Components of a Graph

Abstract: : An algorithm for decomposing a graph into triconnected components is presented. The algorithm requires 0(V + E) time and space when implemented on a random access computer, where V is the number of vertices and E is the number of edges in the graph. The algorithm is both theoretically optimal (to within a constant factor) and efficient in practice.

The CB tree: a practical concurrent self-adjusting search tree

Abstract: We present the CB tree, a counting-based self-adjusting binary search tree in which, as in splay trees, more-frequently accessed items move closer to the root. In a sequential execution, after $$m$$m operations of which $$c(v)$$c(v) access item $$v$$v, an access of $$v$$v traverses a path of length $$\mathcal {O}\left( 1 + \log \frac{m}{c(v)}\right) $$O1+logmc(v) while doing few if any rotations. Unlike the original splay tree, in which each access moves the accessed item all the way to the root via a sequence of rotations, accesses in a CB tree do very few rotations, specifically $$\mathcal {O}\left( n + n\log \frac{m}{n}\right) $$On+nlogmn, during a sequence of $$m$$m operations of which $$n$$n are insertions. This is $$o(1)$$o(1) (subconstant) amortized per operation if $$m \gg n$$m?n. We adapt the CB tree into a scalable concurrent self-adjusting BST. We show experimentally that the concurrent CB tree scales well because it, too, performs few rotations, and therefore self-adjusts without having rotations create a bottleneck. Our evaluation shows that the concurrent CB tree performs better than existing concurrent search trees on non-uniform access sequences derived from real workloads.

Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

Abstract: From the Publisher: Probabilistic Reasoning in Intelligent Systems is a complete andaccessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic. The author distinguishes syntactic and semantic approaches to uncertainty—and offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognition—in short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information. Probabilistic Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.

Nonlinear dimensionality reduction by locally linear embedding.

Abstract: Many areas of science depend on exploratory data analysis and visualization. The need to analyze large amounts of multivariate data raises the fundamental problem of dimensionality reduction: how to discover compact representations of high-dimensional data. Here, we introduce locally linear embedding (LLE), an unsupervised learning algorithm that computes low-dimensional, neighborhood-preserving embeddings of high-dimensional inputs. Unlike clustering methods for local dimensionality reduction, LLE maps its inputs into a single global coordinate system of lower dimensionality, and its optimizations do not involve local minima. By exploiting the local symmetries of linear reconstructions, LLE is able to learn the global structure of nonlinear manifolds, such as those generated by images of faces or documents of text.

The Design and Analysis of Computer Algorithms

Abstract: From the Publisher: With this text, you gain an understanding of the fundamental concepts of algorithms, the very heart of computer science. It introduces the basic data structures and programming techniques often used in efficient algorithms. Covers use of lists, push-down stacks, queues, trees, and graphs. Later chapters go into sorting, searching and graphing algorithms, the string-matching algorithms, and the Schonhage-Strassen integer-multiplication algorithm. Provides numerous graded exercises at the end of each chapter. 0201000296B04062001

Community detection in graphs

Abstract: The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. Such clusters, or communities, can be considered as fairly independent compartments of a graph, playing a similar role like, e. g., the tissues or the organs in the human body. Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs. This problem is very hard and not yet satisfactorily solved, despite the huge effort of a large interdisciplinary community of scientists working on it over the past few years. We will attempt a thorough exposition of the topic, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks.