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Found 4 papers, 1 papers with code
Dual Prices for Frank--Wolfe Algorithms
In this note we observe that for constrained convex minimization problems $\min_{x \in P}f(x)$ over a polytope $P$, dual prices for the linear program $\min_{z \in P} \nabla f(x) z$ obtained from linearization at approximately optimal solutions $x$ have a similar interpretation of rate of change in optimal value as for linear programming, providing a convex form of sensitivity analysis.
Optimization and Control
Blended Conditional Gradients: the unconditioning of conditional gradients
We present a blended conditional gradient approach for minimizing a smooth convex function over a polytope P, combining the Frank--Wolfe algorithm (also called conditional gradient) with gradient-based steps, different from away steps and pairwise steps, but still achieving linear convergence for strongly convex functions, along with good practical performance.
Lazifying Conditional Gradient Algorithms
Conditional gradient algorithms (also often called Frank-Wolfe algorithms) are popular due to their simplicity of only requiring a linear optimization oracle and more recently they also gained significant traction for online learning.
An efficient high-probability algorithm for Linear Bandits
For the linear bandit problem, we extend the analysis of algorithm CombEXP from [R. Combes, M. S. Talebi Mazraeh Shahi, A. Proutiere, and M. Lelarge.
