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.

Numerical Optimization

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.

Machine Learning : A Probabilistic Perspective

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.

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

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.

Shape matching and object recognition using shape contexts

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.

Planning Algorithms: Introductory Material

The results of designing, training, and testing a neural network for the voiced/unvoiced (V/UV) speech classification problem are described A feedforward multilayer backpropagation network was used with six input, ten internal, and two output nodes-for a binary decision The six features are common and easily computed Training was done with 72 frames from two speakers Testing was done with 479 frames from four speakers and resulted in a total of two errors (04%) Thus, a small neural network performs well on the V/UV problem >

Neural networks for voiced/unvoiced speech classification

A central problem in the production of music by computer is how to generate sounds with similar but different timbres; that is, how to synthesize a "family" of instruments that are perceived as distinguishable but similar in timbre. This paper describes a method for synthesizing a family of string-like instruments that proceeds as follows: First, a few actual violin notes are analyzed using linear prediction. Second, a bilinear transformation is applied to the linear prediction model on synthesis, using a recently described algorithm. This yields a computationally efficient way to generate a virtual string orchestra, with violin-, viola-, violoncello-, and bass-like timbres. A realization of a 4.7-minute piece composed by P. Lansky will be played.

Synthesis of Timbral Families by Warped Linear Prediction

We show, numerically, that a single photon trapped by a soliton in a Kerr nonlinear medium can be transferred from one soliton to another when the captor soliton undergoes collision with a second soliton. Soliton collisions can also be used in this way to realize a beam splitter, as well as a mode-separating beam splitter, analogous to the usual polarizing beam splitter. We discuss briefly the feasibility of an optical fiber implementation and possible applications to quantum-information processing.

https://www.cs.princeton.edu/~ken/photon_trapping_transfer09.pdf

Photon trapping and transfer with solitons

The general algebraic equations of root loci for real K are found in polar and Cartesian coordinates. A synthesis method is then suggested which leads to linear equations in the coefficients of the open-loop transfer function when closed-loop poles and their corresponding gains are specified. Equations are also found for the gain corresponding to a given point on the root locus. A superposition theorem is presented which shows how the root loci for two open-loop functions place constraints on the locus for their product. With a knowledge of the simple lower-order loci, this theorem can be used in sketching and constructing root loci.

An analytical approach to root loci

The star-star concentrator problem (SSCP) arises in computer network design. The complexity of this problem is studied, and polynomially solvable and (strongly) NP-complete cases are presented.

Kenneth Steiglitz

Papers

Neural networks for voiced/unvoiced speech classification

Synthesis of Timbral Families by Warped Linear Prediction

Photon trapping and transfer with solitons

An analytical approach to root loci

A Note on the Complexity of the Star-Star Concentrator Problem