Kenneth Steiglitz

Bio: Kenneth Steiglitz is an academic researcher from Princeton University. The author has contributed to research in topics: Signal processing & Very-large-scale integration. The author has an hindex of 46, co-authored 202 publications receiving 14495 citations. Previous affiliations of Kenneth Steiglitz include Telcordia Technologies & Northwestern University.

Some Complexity Results in the Design of Deadlock-Free Packet Switching Networks

Abstract: Deadlocks are very serious system failures and have been observed in existing packet switching networks (PSN’s). Several problems related to the design of deadlock-free PSN’s are investigated here. Polynomial-time algorithms are given for some of these problems, but most of them are shown to be NP-complete or NP-hard, and therefore polynomial-time algorithms are not likely to be found.

A semiring on convex polygons and zero-sum cycle problems

Abstract: Two natural operations on the set of convex polygons are shown to form a closed semiring; the two operations are vector summation and convex hull of the union. Various properties of these operations are investigated. Kleene’s algorithm applied to this closed semiring solves the problem of determining whether a directed graph with two-dimensional labels has a zero-sum cycle or not. This algorithm is shown to run in polynomial time in the special cases of graphs with one-dimensional labels, BTTSP (Backedged Two-Terminal Series-Parallel) graphs, and graphs with bounded labels. The undirected zero-sum cycle problem and the zero-sum simple cycle problem are also investigated.

When can solitons compute

Abstract: We explore the possibility of using soliton interactions in a one-dimensional bulk medium as a basis for a new kind of computer. Such a structure is gateless” { all computations are determined by an input stream of solitons. Intuitively, the key requirement for accomplishing this is that soliton collisions be nonoblivious; that is, solitons should transfer state information during collisions. All the well known systems described by integrable partial di erential equations (PDEs) { the Korteweg-de Vries, sine-Gordon, cubic nonlinear Schrodinger, and perhaps all integrable systems { are oblivious when displacement or phase is used as state. We present a cellular automaton (CA) model, the oblivious soliton machine (OSM), which captures the interaction of solitons in systems described by such integrable PDEs. We then prove that OSMs with either quiescent or periodic backgrounds can do only computation that requires time at most cubic in the input size, and thus are far from being computationuniversal. Next, we de ne a more general class of CA, soliton machines (SMs), which describe systems with more complex interactions. We show that an SM with a quiescent background can have at least the computational power of a nite-tape Turing machine, whereas an SM with a periodic background can be universal. The search for useful nonintegrable (and nonoblivious) systems is challenging: We must rely on numerical solution, collisions may be at best only near-elastic, and collision elasticity and nonobliviousness may be antagonistic qualities. As a step in this direction, we show that the logarithmically nonlinear Schrodinger equation (log-NLS) supports quasi-solitons (gaussons) whose collisions are, in fact, very near-elastic and strongly nonoblivious. It is an open question whether there is a physical system that realizes a computation-universal soliton machine.

A note on 'free accumulation' in VLSI filter architectures

Abstract: Completely pipelined inner product architectures are presented for FIR filtering and linear transformation. The designs use only full adders, organized to form multipliers. By cascading these multiplier structures, no additional area or time is needed to sum their products. The merits of the FFT are briefly reconsidered in the context of high throughput VLSI structures for digital signal processing.

Numerical Optimization

Abstract: Numerical Optimization presents a comprehensive and up-to-date description of the most effective methods in continuous optimization. It responds to the growing interest in optimization in engineering, science, and business by focusing on the methods that are best suited to practical problems. For this new edition the book has been thoroughly updated throughout. There are new chapters on nonlinear interior methods and derivative-free methods for optimization, both of which are used widely in practice and the focus of much current research. Because of the emphasis on practical methods, as well as the extensive illustrations and exercises, the book is accessible to a wide audience. It can be used as a graduate text in engineering, operations research, mathematics, computer science, and business. It also serves as a handbook for researchers and practitioners in the field. The authors have strived to produce a text that is pleasant to read, informative, and rigorous - one that reveals both the beautiful nature of the discipline and its practical side.

Machine Learning : A Probabilistic Perspective

Abstract: Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package--PMTK (probabilistic modeling toolkit)--that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer

Abstract: A digital computer is generally believed to be an efficient universal computing device; that is, it is believed able to simulate any physical computing device with an increase in computation time by at most a polynomial factor. This may not be true when quantum mechanics is taken into consideration. This paper considers factoring integers and finding discrete logarithms, two problems which are generally thought to be hard on a classical computer and which have been used as the basis of several proposed cryptosystems. Efficient randomized algorithms are given for these two problems on a hypothetical quantum computer. These algorithms take a number of steps polynomial in the input size, e.g., the number of digits of the integer to be factored.

Shape matching and object recognition using shape contexts

Abstract: We present a novel approach to measuring similarity between shapes and exploit it for object recognition. In our framework, the measurement of similarity is preceded by: (1) solving for correspondences between points on the two shapes; (2) using the correspondences to estimate an aligning transform. In order to solve the correspondence problem, we attach a descriptor, the shape context, to each point. The shape context at a reference point captures the distribution of the remaining points relative to it, thus offering a globally discriminative characterization. Corresponding points on two similar shapes will have similar shape contexts, enabling us to solve for correspondences as an optimal assignment problem. Given the point correspondences, we estimate the transformation that best aligns the two shapes; regularized thin-plate splines provide a flexible class of transformation maps for this purpose. The dissimilarity between the two shapes is computed as a sum of matching errors between corresponding points, together with a term measuring the magnitude of the aligning transform. We treat recognition in a nearest-neighbor classification framework as the problem of finding the stored prototype shape that is maximally similar to that in the image. Results are presented for silhouettes, trademarks, handwritten digits, and the COIL data set.

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.