Download or read book Computer Oriented Statistical and Optimization Methods written by and published by Krishna Prakashan Media. This book was released on with total page 484 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Statistical Optimization for Geometric Computation written by Kenichi Kanatani and published by Courier Corporation. This book was released on 2005-07-26 with total page 548 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text for graduate students discusses the mathematical foundations of statistical inference for building three-dimensional models from image and sensor data that contain noise--a task involving autonomous robots guided by video cameras and sensors. The text employs a theoretical accuracy for the optimization procedure, which maximizes the reliability of estimations based on noise data. The numerous mathematical prerequisites for developing the theories are explained systematically in separate chapters. These methods range from linear algebra, optimization, and geometry to a detailed statistical theory of geometric patterns, fitting estimates, and model selection. In addition, examples drawn from both synthetic and real data demonstrate the insufficiencies of conventional procedures and the improvements in accuracy that result from the use of optimal methods.
Download or read book Advanced Calculus written by and published by Krishna Prakashan Media. This book was released on with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Optimization Techniques in Statistics written by Jagdish S. Rustagi and published by Elsevier. This book was released on 2014-05-19 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistics help guide us to optimal decisions under uncertainty. A large variety of statistical problems are essentially solutions to optimization problems. The mathematical techniques of optimization are fundamentalto statistical theory and practice. In this book, Jagdish Rustagi provides full-spectrum coverage of these methods, ranging from classical optimization and Lagrange multipliers, to numerical techniques using gradients or direct search, to linear, nonlinear, and dynamic programming using the Kuhn-Tucker conditions or the Pontryagin maximal principle. Variational methods and optimization in function spaces are also discussed, as are stochastic optimization in simulation, including annealing methods. The text features numerous applications, including: Finding maximum likelihood estimates, Markov decision processes, Programming methods used to optimize monitoring of patients in hospitals, Derivation of the Neyman-Pearson lemma, The search for optimal designs, Simulation of a steel mill. Suitable as both a reference and a text, this book will be of interest to advanced undergraduate or beginning graduate students in statistics, operations research, management and engineering sciences, and related fields. Most of the material can be covered in one semester by students with a basic background in probability and statistics. - Covers optimization from traditional methods to recent developments such as Karmarkars algorithm and simulated annealing - Develops a wide range of statistical techniques in the unified context of optimization - Discusses applications such as optimizing monitoring of patients and simulating steel mill operations - Treats numerical methods and applications - Includes exercises and references for each chapter - Covers topics such as linear, nonlinear, and dynamic programming, variational methods, and stochastic optimization
Download or read book Financial Management written by Richard M. Caro and published by Krishna Prakashan Media. This book was released on 1986 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Matrices written by A. R. Vasishtha, A. K. Vasishtha and published by Krishna Prakashan Media. This book was released on with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Organisational Behaviour written by and published by Krishna Prakashan Media. This book was released on with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Krishna s Principles of Management written by and published by Krishna Prakashan Media. This book was released on with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Career Education in India written by S. K. Gupta and published by Mittal Publications. This book was released on 1994 with total page 438 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Computer Oriented Statistical Methods for B B A written by and published by Krishna Prakashan Media. This book was released on with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Practical Mathematical Optimization written by Jan A Snyman and published by Springer. This book was released on 2018-05-02 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents basic optimization principles and gradient-based algorithms to a general audience, in a brief and easy-to-read form. It enables professionals to apply optimization theory to engineering, physics, chemistry, or business economics.
Download or read book Practical Optimization Methods written by M. Asghar Bhatti and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 711 pages. Available in PDF, EPUB and Kindle. Book excerpt: This introductory textbook adopts a practical and intuitive approach, rather than emphasizing mathematical rigor. Computationally oriented books in this area generally present algorithms alone, and expect readers to perform computations by hand, and are often written in traditional computer languages, such as Basic, Fortran or Pascal. This book, on the other hand, is the first text to use Mathematica to develop a thorough understanding of optimization algorithms, fully exploiting Mathematica's symbolic, numerical and graphic capabilities.
Download or read book Statistical Inference Via Convex Optimization written by Anatoli Juditsky and published by Princeton University Press. This book was released on 2020-04-07 with total page 655 pages. Available in PDF, EPUB and Kindle. Book excerpt: This authoritative book draws on the latest research to explore the interplay of high-dimensional statistics with optimization. Through an accessible analysis of fundamental problems of hypothesis testing and signal recovery, Anatoli Juditsky and Arkadi Nemirovski show how convex optimization theory can be used to devise and analyze near-optimal statistical inferences. Statistical Inference via Convex Optimization is an essential resource for optimization specialists who are new to statistics and its applications, and for data scientists who want to improve their optimization methods. Juditsky and Nemirovski provide the first systematic treatment of the statistical techniques that have arisen from advances in the theory of optimization. They focus on four well-known statistical problems—sparse recovery, hypothesis testing, and recovery from indirect observations of both signals and functions of signals—demonstrating how they can be solved more efficiently as convex optimization problems. The emphasis throughout is on achieving the best possible statistical performance. The construction of inference routines and the quantification of their statistical performance are given by efficient computation rather than by analytical derivation typical of more conventional statistical approaches. In addition to being computation-friendly, the methods described in this book enable practitioners to handle numerous situations too difficult for closed analytical form analysis, such as composite hypothesis testing and signal recovery in inverse problems. Statistical Inference via Convex Optimization features exercises with solutions along with extensive appendixes, making it ideal for use as a graduate text.
Download or read book Optimization Models written by Giuseppe C. Calafiore and published by Cambridge University Press. This book was released on 2014-10-31 with total page 651 pages. Available in PDF, EPUB and Kindle. Book excerpt: This accessible textbook demonstrates how to recognize, simplify, model and solve optimization problems - and apply these principles to new projects.
Download or read book Sparse Optimization Theory and Methods written by Yun-Bin Zhao and published by CRC Press. This book was released on 2018-07-04 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: Seeking sparse solutions of underdetermined linear systems is required in many areas of engineering and science such as signal and image processing. The efficient sparse representation becomes central in various big or high-dimensional data processing, yielding fruitful theoretical and realistic results in these fields. The mathematical optimization plays a fundamentally important role in the development of these results and acts as the mainstream numerical algorithms for the sparsity-seeking problems arising from big-data processing, compressed sensing, statistical learning, computer vision, and so on. This has attracted the interest of many researchers at the interface of engineering, mathematics and computer science. Sparse Optimization Theory and Methods presents the state of the art in theory and algorithms for signal recovery under the sparsity assumption. The up-to-date uniqueness conditions for the sparsest solution of underdertemined linear systems are described. The results for sparse signal recovery under the matrix property called range space property (RSP) are introduced, which is a deep and mild condition for the sparse signal to be recovered by convex optimization methods. This framework is generalized to 1-bit compressed sensing, leading to a novel sign recovery theory in this area. Two efficient sparsity-seeking algorithms, reweighted l1-minimization in primal space and the algorithm based on complementary slackness property, are presented. The theoretical efficiency of these algorithms is rigorously analysed in this book. Under the RSP assumption, the author also provides a novel and unified stability analysis for several popular optimization methods for sparse signal recovery, including l1-mininization, Dantzig selector and LASSO. This book incorporates recent development and the author’s latest research in the field that have not appeared in other books.
Download or read book Distributed Optimization and Statistical Learning Via the Alternating Direction Method of Multipliers written by Stephen Boyd and published by Now Publishers Inc. This book was released on 2011 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: Surveys the theory and history of the alternating direction method of multipliers, and discusses its applications to a wide variety of statistical and machine learning problems of recent interest, including the lasso, sparse logistic regression, basis pursuit, covariance selection, support vector machines, and many others.
Download or read book Optimizing Methods in Statistics written by Jagdish S. Rustagi and published by . This book was released on 1971 with total page 488 pages. Available in PDF, EPUB and Kindle. Book excerpt:
