Optimization With Sparsity Inducing Penalties
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Optimization With Sparsity Inducing Penalties
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Author : Francis Bach
language : en
Publisher:
Release Date : 2011-12-23
Optimization With Sparsity Inducing Penalties written by Francis Bach and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-12-23 with Computers categories.
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.
Optimization With Sparsity Inducing Penalties
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Author : Francis Bach
language : en
Publisher:
Release Date : 2012
Optimization With Sparsity Inducing Penalties written by Francis Bach and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with BUSINESS & ECONOMICS categories.
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.
Statistical Learning With Sparsity
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Author : Trevor Hastie
language : en
Publisher: CRC Press
Release Date : 2015-05-07
Statistical Learning With Sparsity written by Trevor Hastie and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-05-07 with Business & Economics categories.
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
Estimation And Testing Under Sparsity
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Author : Sara van de Geer
language : en
Publisher: Springer
Release Date : 2016-06-28
Estimation And Testing Under Sparsity written by Sara van de Geer and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-06-28 with Mathematics categories.
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.
High Dimensional Optimization And Probability
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Author : Ashkan Nikeghbali
language : en
Publisher: Springer Nature
Release Date : 2022-08-04
High Dimensional Optimization And Probability written by Ashkan Nikeghbali and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-04 with Mathematics categories.
This volume presents extensive research devoted to a broad spectrum of mathematics with emphasis on interdisciplinary aspects of Optimization and Probability. Chapters also emphasize applications to Data Science, a timely field with a high impact in our modern society. The discussion presents modern, state-of-the-art, research results and advances in areas including non-convex optimization, decentralized distributed convex optimization, topics on surrogate-based reduced dimension global optimization in process systems engineering, the projection of a point onto a convex set, optimal sampling for learning sparse approximations in high dimensions, the split feasibility problem, higher order embeddings, codifferentials and quasidifferentials of the expectation of nonsmooth random integrands, adjoint circuit chains associated with a random walk, analysis of the trade-off between sample size and precision in truncated ordinary least squares, spatial deep learning, efficient location-based tracking for IoT devices using compressive sensing and machine learning techniques, and nonsmooth mathematical programs with vanishing constraints in Banach spaces. The book is a valuable source for graduate students as well as researchers working on Optimization, Probability and their various interconnections with a variety of other areas. Chapter 12 is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
Optimization And Applications
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Author : Nicholas Olenev
language : en
Publisher: Springer Nature
Release Date : 2023-01-02
Optimization And Applications written by Nicholas Olenev and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-02 with Mathematics categories.
This book constitutes the refereed proceedings of the 13th International Conference on Optimization and Applications, OPTIMA 2022, held in Petrovac, Montenegro, during September 26–30, 2022. The 17 full papers and presented were carefully reviewed and selected from 43 submissions. They were organized into the following as follows: mathematical programming; global optimization; discrete and combinatorial optimization; optimal control; optimization and data analysis; and game theory and mathematical economics.
Practical Applications Of Sparse Modeling
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Author : Irina Rish
language : en
Publisher: MIT Press
Release Date : 2014-09-12
Practical Applications Of Sparse Modeling written by Irina Rish and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-09-12 with Computers categories.
"Sparse modeling is a rapidly developing area at the intersection of statistical learning and signal processing, motivated by the age-old statistical problem of selecting a small number of predictive variables in high-dimensional data sets. This collection describes key approaches in sparse modeling, focusing on its applications in such fields as neuroscience, computational biology, and computer vision. Sparse modeling methods can improve the interpretability of predictive models and aid efficient recovery of high-dimensional unobserved signals from a limited number of measurements. Yet despite significant advances in the field, a number of open issues remain when sparse modeling meets real-life applications. The book discusses a range of practical applications and state-of-the-art approaches for tackling the challenges presented by these applications. Topics considered include the choice of method in genomics applications; analysis of protein mass-spectrometry data; the stability of sparse models in brain imaging applications; sequential testing approaches; algorithmic aspects of sparse recovery; and learning sparse latent models"--Jacket.
Computational Mathematics And Variational Analysis
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Author : Nicholas J. Daras
language : en
Publisher: Springer Nature
Release Date : 2020-06-06
Computational Mathematics And Variational Analysis written by Nicholas J. Daras and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-06 with Mathematics categories.
This volume presents a broad discussion of computational methods and theories on various classical and modern research problems from pure and applied mathematics. Readers conducting research in mathematics, engineering, physics, and economics will benefit from the diversity of topics covered. Contributions from an international community treat the following subjects: calculus of variations, optimization theory, operations research, game theory, differential equations, functional analysis, operator theory, approximation theory, numerical analysis, asymptotic analysis, and engineering. Specific topics include algorithms for difference of monotone operators, variational inequalities in semi-inner product spaces, function variation principles and normed minimizers, equilibria of parametrized N-player nonlinear games, multi-symplectic numerical schemes for differential equations, time-delay multi-agent systems, computational methods in non-linear design of experiments, unsupervised stochastic learning, asymptotic statistical results, global-local transformation, scattering relations of elastic waves, generalized Ostrowski and trapezoid type rules, numerical approximation, Szász Durrmeyer operators and approximation, integral inequalities, behaviour of the solutions of functional equations, functional inequalities in complex Banach spaces, functional contractions in metric spaces.
Computer Vision Eccv 2012
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Author : Andrew Fitzgibbon
language : en
Publisher: Springer
Release Date : 2012-09-26
Computer Vision Eccv 2012 written by Andrew Fitzgibbon and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-09-26 with Computers categories.
The seven-volume set comprising LNCS volumes 7572-7578 constitutes the refereed proceedings of the 12th European Conference on Computer Vision, ECCV 2012, held in Florence, Italy, in October 2012. The 408 revised papers presented were carefully reviewed and selected from 1437 submissions. The papers are organized in topical sections on geometry, 2D and 3D shape, 3D reconstruction, visual recognition and classification, visual features and image matching, visual monitoring: action and activities, models, optimisation, learning, visual tracking and image registration, photometry: lighting and colour, and image segmentation.
Mathematical Analysis And Applications
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Author : Themistocles M. Rassias
language : en
Publisher: Springer Nature
Release Date : 2019-12-12
Mathematical Analysis And Applications written by Themistocles M. Rassias and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-12 with Mathematics categories.
An international community of experts scientists comprise the research and survey contributions in this volume which covers a broad spectrum of areas in which analysis plays a central role. Contributions discuss theory and problems in real and complex analysis, functional analysis, approximation theory, operator theory, analytic inequalities, the Radon transform, nonlinear analysis, and various applications of interdisciplinary research; some are also devoted to specific applications such as the three-body problem, finite element analysis in fluid mechanics, algorithms for difference of monotone operators, a vibrational approach to a financial problem, and more. This volume is useful to graduate students and researchers working in mathematics, physics, engineering, and economics.