Generalized self-concordant analysis of Frank-Wolfe algorithms

Pavel Dvurechensky, Kamil Safin, Shimrit Shtern, Mathias Staudigl*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Web of Science)


Projection-free optimization via different variants of the Frank-Wolfe method has become one of the cornerstones of large scale optimization for machine learning and computational statistics. Numerous applications within these fields involve the minimization of functions with self-concordance like properties. Such generalized self-concordant functions do not necessarily feature a Lipschitz continuous gradient, nor are they strongly convex, making them a challenging class of functions for first-order methods. Indeed, in a number of applications, such as inverse covariance estimation or distance-weighted discrimination problems in binary classification, the loss is given by a generalized self-concordant function having potentially unbounded curvature. For such problems projection-free minimization methods have no theoretical convergence guarantee. This paper closes this apparent gap in the literature by developing provably convergent Frank-Wolfe algorithms with standard O(1/k) convergence rate guarantees. Based on these new insights, we show how these sublinearly convergent methods can be accelerated to yield linearly convergent projection-free methods, by either relying on the availability of a local liner minimization oracle, or a suitable modification of the away-step Frank-Wolfe method.

Original languageEnglish
Pages (from-to)255-323
Number of pages69
JournalMathematical Programming
Issue number1
Early online date29 Jan 2022
Publication statusPublished - Mar 2023


  • Convex programming
  • Frank-Wolfe algorithm
  • Generalized self-concordant functions

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