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Found 5 papers, 4 papers with code
Complexity of Linear Minimization and Projection on Some Sets
The Frank-Wolfe algorithm is a method for constrained optimization that relies on linear minimizations, as opposed to projections.
Projection-Free Adaptive Gradients for Large-Scale Optimization
The complexity in large-scale optimization can lie in both handling the objective function and handling the constraint set.
Boosting Frank-Wolfe by Chasing Gradients
The Frank-Wolfe algorithm has become a popular first-order optimization algorithm for it is simple and projection-free, and it has been successfully applied to a variety of real-world problems.
Revisiting the Approximate Carathéodory Problem via the Frank-Wolfe Algorithm
The approximate Carath\'eodory theorem states that given a compact convex set $\mathcal{C}\subset\mathbb{R}^n$ and $p\in\left[2,+\infty\right[$, each point $x^*\in\mathcal{C}$ can be approximated to $\epsilon$-accuracy in the $\ell_p$-norm as the convex combination of $\mathcal{O}(pD_p^2/\epsilon^2)$ vertices of $\mathcal{C}$, where $D_p$ is the diameter of $\mathcal{C}$ in the $\ell_p$-norm.
Blended Matching Pursuit
Matching pursuit algorithms are an important class of algorithms in signal processing and machine learning.
