Ergebnisse für: alternating optimization
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Machine learning heavily relies on optimization algorithms to solve its learning models. Constrained problems constitute a major type of optimization problem, and the alternating direction method of multipliers (ADMM) is a commonly used algorithm to solve constrained problems, especially linearly co...
This book brings together groundbreaking research papers authored by distinguished experts and scholars. Each contribution delves into innovative aspects of splitting optimization—ranging from theoretical advancements and methodological developments to a diverse array of applications. The book off...
This book provides an in-depth exploration of nonsmooth optimization, covering foundational algorithms, theoretical insights, and a wide range of applications. Nonsmooth optimization, characterized by nondifferentiable objective functions or constraints, plays a crucial role across various fields, i...
This book demonstrates the optimal adversarial attacks against several important signal processing algorithms. Through presenting the optimal attacks in wireless sensor networks, array signal processing, principal component analysis, etc, the authors reveal the robustness of the signal pro...
This book is about computational methods based on operator splitting.   It consists of twenty-three chapters written by recognized splitting method contributors and practitioners, and covers a vast spectrum of topics and application areas, including computational mechanics, computational physic...
Machine learning heavily relies on optimization algorithms to solve its learning models. Constrained problems constitute a major type of optimization problem, and the alternating direction method of multipliers (ADMM) is a commonly used algorithm to solve constrained problems, especially linearly co...
Machine learning heavily relies on optimization algorithms to solve its learning models. Constrained problems constitute a major type of optimization problem, and the alternating direction method of multipliers (ADMM) is a commonly used algorithm to solve constrained problems, especially linearly co...
The book on The General Theory of Electrical Machines, by B. Adkins, which was published in 1957, has been well received, as a manual containing the theories on which practical methods of calculating machine performance can be based, and as a text-book for advanced students. Since 1957, many importa...
This book brings together groundbreaking research papers authored by distinguished experts and scholars. Each contribution delves into innovative aspects of splitting optimization—ranging from theoretical advancements and methodological developments to a diverse array of applications. The book off...
The book on The General Theory of Electrical Machines, by B. Adkins, which was published in 1957, has been well received, as a manual containing the theories on which practical methods of calculating machine performance can be based, and as a text-book for advanced students. Since 1957, many importa...
This book provides an in-depth exploration of nonsmooth optimization, covering foundational algorithms, theoretical insights, and a wide range of applications. Nonsmooth optimization, characterized by nondifferentiable objective functions or constraints, plays a crucial role across various fields, i...
The 16th International Symposium on Graph Drawing (GD 2008) was held in Hersonissos, near Heraklion, Crete, Greece, September 21-24, 2008, and was attended by 91 participants from 19 countries. In response to the call for papers the Program Committee received 83 s- missions,eachdescribing originalre...
The 16th International Symposium on Graph Drawing (GD 2008) was held in Hersonissos, near Heraklion, Crete, Greece, September 21-24, 2008, and was attended by 91 participants from 19 countries. In response to the call for papers the Program Committee received 83 s- missions,eachdescribing originalre...
This book is about computational methods based on operator splitting.   It consists of twenty-three chapters written by recognized splitting method contributors and practitioners, and covers a vast spectrum of topics and application areas, including computational mechanics, computational physic...
This book demonstrates the optimal adversarial attacks against several important signal processing algorithms. Through presenting the optimal attacks in wireless sensor networks, array signal processing, principal component analysis, etc, the authors reveal the robustness of the signal pro...
This book investigates compressive sensing techniques to provide a robust and general framework for network data analytics. The goal is to introduce a compressive sensing framework for missing data interpolation, anomaly detection, data segmentation and activity recognition, and to demonstrate its b...
This book demonstrates the optimal adversarial attacks against several important signal processing algorithms. Through presenting the optimal attacks in wireless sensor networks, array signal processing, principal component analysis, etc, the authors reveal the robustness of the signal pro...
Some of the most beautiful studies in Mathematics are related to Symmetry and Geometry. For this reason, we select here some contributions about such aspects and Discrete Geometry. As we know, Symmetry in a system means invariance of its elements under conditions of transformations. When we consider...
Target object detection and identification are among the primary uses for a remote sensing system. This is crucial in several fields, including environmental and urban monitoring, hazard and disaster management, and defense and military. In recent years, these analyses have used the tremendous amoun...
This book is about computational methods based on operator splitting.   It consists of twenty-three chapters written by recognized splitting method contributors and practitioners, and covers a vast spectrum of topics and application areas, including computational mechanics, computational physic...