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Found 11 papers, 2 papers with code
Self-concordant analysis of Frank-Wolfe algorithm
Projection-free optimization via different variants of the Frank-Wolfe (FW) method has become one of the cornerstones in optimization for machine learning since in many cases the linear minimization oracle is much cheaper to implement than projections and some sparsity needs to be preserved.
A conditional gradient homotopy method with applications to Semidefinite Programming
We propose a new homotopy-based conditional gradient method for solving convex optimization problems with a large number of simple conic constraints.
A privacy-preserving distributed computational approach for distributed locational marginal prices
An important issue in today's electricity markets is the management of flexibilities offered by new practices, such as smart home appliances or electric vehicles.
First-Order Methods for Convex Optimization
In this survey we cover a number of key developments in gradient-based optimization methods.
Computing Dynamic User Equilibrium on Large-Scale Networks Without Knowing Global Parameters
Dynamic user equilibrium (DUE) is a Nash-like solution concept describing an equilibrium in dynamic traffic systems over a fixed planning period.
Self-Concordant Analysis of Frank-Wolfe Algorithms
Projection-free optimization via different variants of the Frank-Wolfe (FW), a. k. a.
Generalized Self-concordant Hessian-barrier algorithms
Many problems in statistical learning, imaging, and computer vision involve the optimization of a non-convex objective function with singularities at the boundary of the feasible set.
Forward-backward-forward methods with variance reduction for stochastic variational inequalities
We develop a new stochastic algorithm with variance reduction for solving pseudo-monotone stochastic variational inequalities.
Hessian barrier algorithms for linearly constrained optimization problems
In the case of linearly constrained quadratic programs (not necessarily convex), we also show that the method's convergence rate is $\mathcal{O}(1/k^\rho)$ for some $\rho\in(0, 1]$ that depends only on the choice of kernel function (i. e., not on the problem's primitives).
Multi-agent online learning in time-varying games
We examine the long-run behavior of multi-agent online learning in games that evolve over time.
On the convergence of gradient-like flows with noisy gradient input
In the vanishing noise limit, we show that the dynamics converge to the solution set of the underlying problem (a. s.).
