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Splitting Algorithms, Modern Operator Theory, and Applications

Overview of attention for book
Cover of 'Splitting Algorithms, Modern Operator Theory, and Applications'

Table of Contents

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    Book Overview
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    Chapter 1 Convergence Rate of Proximal Inertial Algorithms Associated with Moreau Envelopes of Convex Functions
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    Chapter 2 Constraint Splitting and Projection Methods for Optimal Control of Double Integrator
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    Chapter 3 Numerical Explorations of Feasibility Algorithms for Finding Points in the Intersection of Finite Sets
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    Chapter 4 Variable Metric ADMM for Solving Variational Inequalities with Monotone Operators over Affine Sets
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    Chapter 5 Regularization of Ill-Posed Problems with Non-negative Solutions
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    Chapter 6 Characterizations of Super-Regularity and Its Variants
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    Chapter 7 The Inverse Function Theorems of L. M. Graves
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    Chapter 8 Block-Wise Alternating Direction Method of Multipliers with Gaussian Back Substitution for Multiple-Block Convex Programming
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    Chapter 9 Variable Metric Algorithms Driven by Averaged Operators
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    Chapter 10 A Glimpse at Pointwise Asymptotic Stability for Continuous-Time and Discrete-Time Dynamics
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    Chapter 11 A Survey on Proximal Point Type Algorithms for Solving Vector Optimization Problems
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    Chapter 12 Non-polyhedral Extensions of the Frank and Wolfe Theorem
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    Chapter 13 A Note on the Equivalence of Operator Splitting Methods
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    Chapter 14 Quasidensity: A Survey and Some Examples
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    Chapter 15 On the Acceleration of Forward-Backward Splitting via an Inexact Newton Method
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    Chapter 16 Hierarchical Convex Optimization by the Hybrid Steepest Descent Method with Proximal Splitting Operators—Enhancements of SVM and Lasso
Attention for Chapter 16: Hierarchical Convex Optimization by the Hybrid Steepest Descent Method with Proximal Splitting Operators—Enhancements of SVM and Lasso
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Citations

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Chapter title
Hierarchical Convex Optimization by the Hybrid Steepest Descent Method with Proximal Splitting Operators—Enhancements of SVM and Lasso
Chapter number 16
Book title
Splitting Algorithms, Modern Operator Theory, and Applications
Published in
arXiv, January 2019
DOI 10.1007/978-3-030-25939-6_16
Book ISBNs
978-3-03-025938-9, 978-3-03-025939-6
Authors

Isao Yamada, Masao Yamagishi, Yamada, Isao, Yamagishi, Masao

View on publisher site
X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
 Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 2 67%
Student > Ph. D. Student 1 33%
Librarian 1 33%
Readers by discipline Count As %
Computer Science 3 100%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 July 2022.
All research outputs
#17,734,890
of 22,774,233 outputs
Outputs from arXiv
#439,263
of 934,788 outputs
Outputs of similar age
#303,410
of 436,132 outputs
Outputs of similar age from arXiv
#12,299
of 24,457 outputs
Altmetric has tracked 22,774,233 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 934,788 research outputs from this source. They receive a mean Attention Score of 3.9. This one is in the 42nd percentile – i.e., 42% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 436,132 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 24,457 others from the same source and published within six weeks on either side of this one. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.

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