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.

Exact, Approximate, and Guaranteed Accuracy Algorithms for the Flow-Shop Problem n / 2 / F / F¯

Abstract: Improved exact and approximate algorithms for the n-job two-machine mean finishing time flow-shop problem, n/2JF/P, are presented While other researchers have used a variety of approximate methods to generate suboptimal solutions and branch-and-bound algorithms to gen- erate exact solutmns to sequencing problems, thin work demonstrates the computatmnal effectiveness of couphng the two methods to generate solutmns with a guaranteed accuracy. The computational reqmrements of exact, approximate, and guaranteed accuracy algorithms are compared expem- mentally on a set of test problems ranging in size from 10 to 50 jobs The approach is readily apphca- ble to other sequencing problems

Digital Signal Processing Applications of Systolic Algorithms

Abstract: VLSI structures and algorithms are given for bit-serial FIR filtering, IIR filtering, and convolution. We also present a bit-parallel FIR filter design. The structures are highly regular, programmable, and area-efficient. In fact, we will show that most are within log factors of asymptotic optimality. These structures are completely pipelined; that is, the throughput rate (bits/second) is independent of both word size and filter length. This is to be contrasted with algorithms designed and implemented in terms of, say, multipliers and adders whose throughput rates may depend on word length.

Time-Domain Approximation by Iterative Methods

Abstract: An iterative procedure is presented which permits the determination of a rational transfer function in the Laplace transform variable s which is optimal with respect to given input and output time-functions. The optimal system of a particular order is defined as the one whose output when subjected to the known input function is nearest in the time integral square sense to the desired output function. The method is thus applicable to a number of problems involving the minimization of an integral square error. To illustrate the technique, a set of optimal lumped-parameter delay lines is synthesized and their characteristics investigated; the behavior and convergence of the iteration in these problems is also studied. A comparison of other iterative methods applicable to the same problems leads to the conclusion that the proposed procedure has real advantages in computational simplicity and speed of convergence.

The use of time-domain selection for improved linear prediction

Abstract: We show by theoretical argument and by experiment with both synthetic and real data that selection of an undriven segment of voiced speech for analysis by linear predictive coding (LPC) gives more accurate estimates of the poles of the vocal-tract model. In the case of voiced nasal phonemes, this technique provides a simple algorithm for separately determining the poles and the zeros in the model and illustrates the desirability of identifying the portions of the speech wave during which there is a significant driving input. A key problem which remains is the development of a practical algorithm for selecting such segments for analysis.

The Expression of Algorithms by Charts

Abstract: This paper discusses the expression of algorithms by flowcharts, and in particular by flowcharts without explicit go-to's (D-charts). For this purpose we introduce a machine independent definition of algorithm which is broader than usual. Our conclusion is that D- charts are in one technical sense more restrictive than general flowcharts, but not if one allows the introduction of additional variables which represent a history of control flow. The term "algorithm" is used in many different ways. Sometimes we speak of an algorithm as a process in the abstract, without reference to a particular computer. It is in this sense, for example, that we speak of the "radix exchange sort algorithm," or the "simplex algorithm." Often we identify an algorithm with a particular se- quence of instructions for a particular computer. In this paper we shall present a new definition of algorithm which emphasizes the sequence of commands associated with a particular "input." We then define the notion "expression" of algorithms by general flowcharts and flowcharts without explicit go-to's (D-charts). Some theorems are given which exhibit some of the rela- tionships between algorithms, flowcharts, and D-charts.

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.