Skip to main navigation
Skip to search
Skip to main content
Maastricht University Home
Support & FAQ
Link opens in a new tab
Search content at Maastricht University
Home
Researchers
Publications
Activities
Press/Media
Prizes
Organisations
Dataset/Software
Projects
Generalized self-concordant analysis of Frank-Wolfe algorithms
Pavel Dvurechensky
, Kamil Safin
, Shimrit Shtern
, Mathias Staudigl
*
*
Corresponding author for this work
Department of Advanced Computing Sciences
Department of Advanced Computing Sciences
Research output
:
Contribution to journal
›
Article
›
Academic
›
peer-review
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Generalized self-concordant analysis of Frank-Wolfe algorithms'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
Self-concordant Functions
100%
Frank-Wolfe Algorithm
100%
Frank-Wolfe Method
100%
Oracle
50%
Convergence Rate
50%
Minimization Problem
50%
Large-scale Optimization
50%
Liner
50%
Convergence Guarantee
50%
Self-concordance
50%
Learning Statistics
50%
Binary Classification
50%
Optimization for Machine Learning
50%
Projection-free Optimization
50%
Unbounded Curvature
50%
Projection Method
50%
Strongly Convex
50%
First-order Methods
50%
Lipschitz Continuous Gradient
50%
Rate Guarantees
50%
Inverse Covariance Estimation
50%
Computational Statistics
50%
Convergent Method
50%
Theoretical Convergence
50%
Distance Discrimination
50%
Class Function
50%
Projection-free
50%
INIS
algorithms
100%
minimization
100%
applications
66%
optimization
66%
convergence
66%
availability
33%
yields
33%
losses
33%
classification
33%
modifications
33%
distance
33%
statistics
33%
machine learning
33%
liners
33%
Mathematics
Wolfe Algorithm
100%
Covariance
50%
Convergence Rate
50%
Computational Statistics
50%
Binary Classification
50%