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.

On the complexity of local search for the traveling salesman problem

Abstract: It is shown that, unless $P = NP$, local search algorithms for the traveling salesman problem having polynomial time complexity per iteration will generate solutions arbitrarily far from the optimal.

Some complexity issues in digital signal processing

Abstract: Over the past decade a large class of problems, called NP-complete [5], have been shown to be equivalent in the sense that if a fast algorithm can be found for one, fast algorithms can be found for all. At the same time, despite much effort, no fast algorithms have been found for any, and these problems are widely regarded as intractable. This class includes such notoriously difficult problems as the traveling salesman problem, graph coloring, and satisfiability of Boolean expressions. Using FIR filter implementation as an illustration, we describe some problems in digital signal processing that are NP-complete. These include: 1) minimize the number of additions needed to implement a fixed FIR filter; 2) minimize the number of registers needed to implement a fixed FIR filter; and 3) minimize the time to perform the additions of such an FIR filter using P adders. Large-seale instances of such problems may become important with the use of programmable chips to implement signal processing. Our main purpose in this paper is to illustrate the usefulness of asymptotic complexity theory in the field of digital signal processing. The theory discriminates between tractable and intractable problems, Sometimes identifies fast algorithms for the former, and justifies heuristics for the latter.

Energy-Exchange Interactions between Colliding Vector Solitons

Abstract: We demonstrate experimentally collisions between vector (Manakov-like) solitons that involve energy exchange at large collision angles, for which scalar solitons pass through one another practically unaffected.

Phase unwrapping by factorization

Abstract: An algorithm for the numerical factorization of very high degree but well-conditioned polynomials is developed. This is used to factor the z-transform of finite-length signals, and the zeros are used to calculate the unwrapped phase. The method has been tested on signals up to 512 points in length. A complete Fortran 77 program is given for the case of a real-valued signal. Two related analytical issues are treated. First, the interpretation of phase unwrapping as an interpolation problem is discussed. Second, an explanation is given for the observed numerical difficulties in the method of phase unwrapping using adaptive integration of the phase derivative. The trouble is due to the clustering of the zeros of high degree polynomials near the unit circle.

The design of wide-band recursive and nonrecursive digital differentiators

Abstract: Designs for recursive and nonrecursive wide-band differentiators are presented. The coefficients for the recursive differentiators were optimally chosen to minimize a square-error criterion based on the magnitude of the frequency response. The coefficients for the nonrecursive differentiators were chosen using a frequency sampling technique. One or more of the coefficients were optimally selected to minimize the peak absolute error between the obtained frequency response and the response of an ideal differentiator. The frequency response characteristics of the recursive differentiators had small magnitude errors but significant phase errors. The nonrecursive differentiators required on the order of 16 to 32 terms for the magnitude error of the frequency response to be as small as the magnitude errors for the recursive differentiators; however, there were no phase errors for the nonrecursive case. The delay of the recursive differentiators was small compared to the delay of the nonrecursive differentiators.

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.