Download or read book Sparsity written by Jaroslav Nešetřil and published by Springer Science & Business Media. This book was released on 2012-04-24 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first book devoted to the systematic study of sparse graphs and sparse finite structures. Although the notion of sparsity appears in various contexts and is a typical example of a hard to define notion, the authors devised an unifying classification of general classes of structures. This approach is very robust and it has many remarkable properties. For example the classification is expressible in many different ways involving most extremal combinatorial invariants. This study of sparse structures found applications in such diverse areas as algorithmic graph theory, complexity of algorithms, property testing, descriptive complexity and mathematical logic (homomorphism preservation,fixed parameter tractability and constraint satisfaction problems). It should be stressed that despite of its generality this approach leads to linear (and nearly linear) algorithms. Jaroslav Nešetřil is a professor at Charles University, Prague; Patrice Ossona de Mendez is a CNRS researcher et EHESS, Paris. This book is related to the material presented by the first author at ICM 2010.

Download or read book Statistical Learning with Sparsity written by Trevor Hastie and published by CRC Press. This book was released on 2015-05-07 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover New Methods for Dealing with High-Dimensional DataA sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underl

Download or read book Sparsity Methods for Systems and Control written by Masaaki Nagahara and published by . This book was released on 2020-09-30 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: The method of sparsity has been attracting a lot of attention in the fields related not only to signal processing, machine learning, and statistics, but also systems and control. The method is known as compressed sensing, compressive sampling, sparse representation, or sparse modeling. More recently, the sparsity method has been applied to systems and control to design resource-aware control systems. This book gives a comprehensive guide to sparsity methods for systems and control, from standard sparsity methods in finite-dimensional vector spaces (Part I) to optimal control methods in infinite-dimensional function spaces (Part II). The primary objective of this book is to show how to use sparsity methods for several engineering problems. For this, the author provides MATLAB programs by which the reader can try sparsity methods for themselves. Readers will obtain a deep understanding of sparsity methods by running these MATLAB programs. Sparsity Methods for Systems and Control is suitable for graduate level university courses, though it should also be comprehendible to undergraduate students who have a basic knowledge of linear algebra and elementary calculus. Also, especially part II of the book should appeal to professional researchers and engineers who are interested in applying sparsity methods to systems and control.

Download or read book Sparsity and Its Applications written by David J. Evans and published by CUP Archive. This book was released on 1985-01-10 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt: Good,No Highlights,No Markup,all pages are intact, Slight Shelfwear,may have the corners slightly dented, may have slight color changes/slightly damaged spine.

Download or read book Estimation and Testing Under Sparsity written by Sara van de Geer and published by Springer. This book was released on 2016-06-28 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: Taking the Lasso method as its starting point, this book describes the main ingredients needed to study general loss functions and sparsity-inducing regularizers. It also provides a semi-parametric approach to establishing confidence intervals and tests. Sparsity-inducing methods have proven to be very useful in the analysis of high-dimensional data. Examples include the Lasso and group Lasso methods, and the least squares method with other norm-penalties, such as the nuclear norm. The illustrations provided include generalized linear models, density estimation, matrix completion and sparse principal components. Each chapter ends with a problem section. The book can be used as a textbook for a graduate or PhD course.

Download or read book Algorithms for Sparsity Constrained Optimization written by Sohail Bahmani and published by Springer Science & Business Media. This book was released on 2013-10-07 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis demonstrates techniques that provide faster and more accurate solutions to a variety of problems in machine learning and signal processing. The author proposes a "greedy" algorithm, deriving sparse solutions with guarantees of optimality. The use of this algorithm removes many of the inaccuracies that occurred with the use of previous models.

Download or read book Study on Signal Detection and Recovery Methods with Joint Sparsity written by Xueqian Wang and published by Springer Nature. This book was released on 2023-09-30 with total page 135 pages. Available in PDF, EPUB and Kindle. Book excerpt: The task of signal detection is deciding whether signals of interest exist by using their observed data. Furthermore, signals are reconstructed or their key parameters are estimated from the observations in the task of signal recovery. Sparsity is a natural characteristic of most of signals in practice. The fact that multiple sparse signals share the common locations of dominant coefficients is called by joint sparsity. In the context of signal processing, joint sparsity model results in higher performance of signal detection and recovery. This book focuses on the task of detecting and reconstructing signals with joint sparsity. The main contents include key methods for detection of joint sparse signals and their corresponding theoretical performance analysis, and methods for joint sparse signal recovery and their application in the context of radar imaging.

Download or read book Sparse Sensing and Sparsity Sensed in Multi sensor Array Applications written by Xiangrong Wang and published by Springer Nature. This book was released on with total page 387 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Analyticity and Sparsity in Uncertainty Quantification for PDEs with Gaussian Random Field Inputs written by Dinh Dũng and published by Springer Nature. This book was released on 2023-11-16 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: The present book develops the mathematical and numerical analysis of linear, elliptic and parabolic partial differential equations (PDEs) with coefficients whose logarithms are modelled as Gaussian random fields (GRFs), in polygonal and polyhedral physical domains. Both, forward and Bayesian inverse PDE problems subject to GRF priors are considered. Adopting a pathwise, affine-parametric representation of the GRFs, turns the random PDEs into equivalent, countably-parametric, deterministic PDEs, with nonuniform ellipticity constants. A detailed sparsity analysis of Wiener-Hermite polynomial chaos expansions of the corresponding parametric PDE solution families by analytic continuation into the complex domain is developed, in corner- and edge-weighted function spaces on the physical domain. The presented Algorithms and results are relevant for the mathematical analysis of many approximation methods for PDEs with GRF inputs, such as model order reduction, neural network and tensor-formatted surrogates of parametric solution families. They are expected to impact computational uncertainty quantification subject to GRF models of uncertainty in PDEs, and are of interest for researchers and graduate students in both, applied and computational mathematics, as well as in computational science and engineering.

Download or read book Inverse Problems with Sparsity Constraints written by Dennis Trede and published by Logos Verlag Berlin GmbH. This book was released on 2010 with total page 137 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis contributes to the field of inverse problems with sparsity constraints. Since the pioneering work by Daubechies, Defries and De Mol in 2004, methods for solving operator equations with sparsity constraints play a central role in the field of inverse problems. This can be explained by the fact that the solutions of many inverse problems have a sparse structure, in other words, they can be represented using only finitely many elements of a suitable basis or dictionary. Generally, to stably solve an ill-posed inverse problem one needs additional assumptions on the unknown solution--the so-called source condition. In this thesis, the sparseness stands for the source condition, and with that in mind, stability results for two different approximation methods are deduced, namely, results for the Tikhonov regularization with a sparsity-enforcing penalty and for the orthogonal matching pursuit. The practical relevance of the theoretical results is shown with two examples of convolution type, namely, an example from mass spectrometry and an example from digital holography of particles.

Download or read book Variational Source Conditions Quadratic Inverse Problems Sparsity Promoting Regularization written by Jens Flemming and published by Springer. This book was released on 2018-09-08 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book collects and contributes new results on the theory and practice of ill-posed inverse problems. Different notions of ill-posedness in Banach spaces for linear and nonlinear inverse problems are discussed not only in standard settings but also in situations up to now not covered by the literature. Especially, ill-posedness of linear operators with uncomplemented null spaces is examined.Tools for convergence rate analysis of regularization methods are extended to a wider field of applicability. It is shown that the tool known as variational source condition always yields convergence rate results. A theory for nonlinear inverse problems with quadratic structure is developed as well as corresponding regularization methods. The new methods are applied to a difficult inverse problem from laser optics.Sparsity promoting regularization is examined in detail from a Banach space point of view. Extensive convergence analysis reveals new insights into the behavior of Tikhonov-type regularization with sparsity enforcing penalty.

Download or read book Sparsity Based Multipath Exploitation for Through the Wall Radar Imaging written by Michael Leigsnering and published by Springer. This book was released on 2018-02-16 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis reports on sparsity-based multipath exploitation methods for through-the-wall radar imaging. Multipath creates ambiguities in the measurements provoking unwanted ghost targets in the image. This book describes sparse reconstruction methods that are not only suppressing the ghost targets, but using multipath to one’s advantage. With adopting the compressive sensing principle, fewer measurements are required for image reconstruction as compared to conventional techniques. The book describes the development of a comprehensive signal model and some associated reconstruction methods that can deal with many relevant scenarios, such as clutter from building structures, secondary reflections from interior walls, as well as stationary and moving targets, in urban radar imaging. The described methods are evaluated here using simulated as well as measured data from semi-controlled laboratory experiments.

Download or read book A risk gain sparsity optimization approach written by Alessandra Congedo and published by Roma TrE-Press. This book was released on 2024-06-12 with total page 34 pages. Available in PDF, EPUB and Kindle. Book excerpt: Uno dei principi fondamentali dei modelli di selezione del portafoglio è la minimizzazione del rischio attraverso la diversificazione degli investimenti. Tuttavia, i benefici della diversificazione si riducono in presenza di un'elevata correlazione tra gli asset. È noto che la diversificazione attraverso l'uso di portafogli più ampi non è il modo migliore per ottenere un miglioramento della performance fuori campione. Inoltre, l'inclusione di un numero elevato di posizioni nel portafoglio aumenta i costi di gestione e di transazione. Mentre i modelli classici di selezione del portafoglio si concentrano sulla minimizzazione del rischio e sulla massimizzazione del rendimento, lo scopo di questo lavoro è quello di includere un terzo obiettivo: la norma-1. Ciò consente di selezionare portafogli sparsi, cioè con un numero limitato di attività, che sono più facili da gestire e consentono di ottenere buoni risultati in termini di rischio-rendimento. La nostra analisi empirica si basa su un dataset di riferimento disponibile pubblicamente e spesso utilizzato in letteratura. DOI: 10.13134/979-12-5977-332-6

Download or read book Overcoming Data Sparsity A Machine Learning Approach to Track the Real Time Impact of COVID 19 in Sub Saharan Africa written by Karim Barhoumi and published by International Monetary Fund. This book was released on 2022-05-06 with total page 23 pages. Available in PDF, EPUB and Kindle. Book excerpt: The COVID-19 crisis has had a tremendous economic impact for all countries. Yet, assessing the full impact of the crisis has been frequently hampered by the delayed publication of official GDP statistics in several emerging market and developing economies. This paper outlines a machine-learning framework that helps track economic activity in real time for these economies. As illustrative examples, the framework is applied to selected sub-Saharan African economies. The framework is able to provide timely information on economic activity more swiftly than official statistics.

Download or read book Optimization with Sparsity Inducing Penalties written by Francis Bach and published by . This book was released on 2011-12-23 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sparse estimation methods are aimed at using or obtaining parsimonious representations of data or models. They were first dedicated to linear variable selection but numerous extensions have now emerged such as structured sparsity or kernel selection. It turns out that many of the related estimation problems can be cast as convex optimization problems by regularizing the empirical risk with appropriate nonsmooth norms. Optimization with Sparsity-Inducing Penalties presents optimization tools and techniques dedicated to such sparsity-inducing penalties from a general perspective. It covers proximal methods, block-coordinate descent, reweighted ?2-penalized techniques, working-set and homotopy methods, as well as non-convex formulations and extensions, and provides an extensive set of experiments to compare various algorithms from a computational point of view. The presentation of Optimization with Sparsity-Inducing Penalties is essentially based on existing literature, but the process of constructing a general framework leads naturally to new results, connections and points of view. It is an ideal reference on the topic for anyone working in machine learning and related areas.

Download or read book Data Driven Science and Engineering written by Steven L. Brunton and published by Cambridge University Press. This book was released on 2022-05-05 with total page 615 pages. Available in PDF, EPUB and Kindle. Book excerpt: A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.

Download or read book A Study of Sparsity Factors written by Idaho. Legislative Council. Committee on Sparsity Factors and published by . This book was released on 1964 with total page 52 pages. Available in PDF, EPUB and Kindle. Book excerpt: