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Found 4 papers, 1 papers with code
Linearization Algorithms for Fully Composite Optimization
This paper studies first-order algorithms for solving fully composite optimization problems over convex and compact sets.
Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization
We propose a stochastic conditional gradient method (CGM) for minimizing convex finite-sum objectives formed as a sum of smooth and non-smooth terms.
A first-order primal-dual method with adaptivity to local smoothness
We consider the problem of finding a saddle point for the convex-concave objective $\min_x \max_y f(x) + \langle Ax, y\rangle - g^*(y)$, where $f$ is a convex function with locally Lipschitz gradient and $g$ is convex and possibly non-smooth.
Conditional gradient methods for stochastically constrained convex minimization
We propose two novel conditional gradient-based methods for solving structured stochastic convex optimization problems with a large number of linear constraints.
