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.

Identifying a minimum cut and/or a maximum flow using balancing of vertex excesses

Abstract: A representation of a network having vertices connected by arcs is provided, where the network exhibits a pseudoflow. Arcs on which moves are to be performed are iteratively chosen for balancing excesses of vertices across the arcs until a stopping rule is satisfied. After the stopping rule is satisfied, further processing is performed to identify at least one of a minimum cut and/or maximum flow.

Pólya’s Theory of Counting

Abstract: February 2. Polya’s title for this part of the course was actually “Counting Configuration Non-Equivalent with Respect to a Given Permutation Group”. Just about everybody else refers to it as “Polya’s Theory of Counting”. This later title is somewhat easier to remember, though not as indicative of the content. We’ll stick to the simpler title; for the content, read on!

Toward Efficient Unstructured Multigrid Preprocessing (Extended Abstract)

Abstract: The multigrid method is a general and powerful means of accelerating the convergence of discrete iterative methods for solving partial differential equations (PDEs) and similar problems. The adaptation of the multigrid method to unstructured meshes is important in the solution of problems with complex geometries. Unfortunately, multigrid schemes on unstructured meshes require significantly more preprocessing than on structured meshes. In fact, preprocessing can be a major part of the solution task, and for many applications, must be done repeatedly. In addition, the large computational requirements of realistic PDEs, accurately discretized on unstructured meshes, make such computations candidates for parallel or distributed processing, adding problem partitioning as a preprocessing task.

Nested Set Union

Abstract: We consider a version of the classic disjoint set union (union-find) problem in which there are two partitions of the elements, rather than just one, but restricted such that one partition is a refinement of the other. We call this the nested set union problem. This problem occurs in a new algorithm to find dominators in a flow graph. One can solve the problem by using two instances of a data structure for the classical problem, but it is natural to ask whether these instances can be combined. We show that the answer is yes: the nested problem can be solved by extending the classic solution to support two nested partitions, at the cost of at most a few bits of storage per element and a small constant overhead in running time. Our solution extends to handle any constant number of nested partitions.

A Tight Analysis of Slim Heaps and Smooth Heaps

Abstract: The smooth heap and the closely related slim heap are recently invented self-adjusting implementations of the heap (priority queue) data structure. We analyze the efficiency of these data structures. We obtain the following amortized bounds on the time per operation: $O(1)$ for make-heap, insert, find-min, and meld; $O(\log\log n)$ for decrease-key; and $O(\log n)$ for delete-min and delete, where $n$ is the current number of items in the heap. These bounds are tight not only for smooth and slim heaps but for any heap implementation in Iacono and Ozkan's pure heap model, intended to capture all possible "self-adjusting" heap implementations. Slim and smooth heaps are the first known data structures to match Iacono and Ozkan's lower bounds and to satisfy the constraints of their model. Our analysis builds on Pettie's insights into the efficiency of pairing heaps, a classical self-adjusting heap implementation.

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.