We provide stronger and more general primal-dual convergence results for Frank-Wolfe-
type algorithms (aka conditional gradient) for constrained convex optimization, enabled by a …

Convex optimization is at the core of many of today's analysis tools for large datasets, and in
particular machine learning methods. In this thesis we will study the general setting of …

In this article, we consider the problem of tuning the edge weights of a networked system
described by linear time-invariant dynamics. We assume that the topology of the underlying …

For the general problem of minimizing a convex function over a compact convex domain, we
will investigate a simple iterative approximation algorithm based on the method by Frank & …

We consider joint channel estimation and faulty antenna detection for massive multiple-input
multiple-output systems operating in time-division duplexing mode. For systems with faulty …

We consider an abstract class of optimization problems that are parameterized concavely in
a single parameter, and show that the solution path along the parameter can always be …

Regularized loss minimization, where a statistical model is obtained from minimizing the
sum of a loss function and weighted regularization terms, is still in widespread use in …

Algorithmically, many machine learning tasks boil down to solving parameterized
optimization problems. Finding good values for the parameters has significant influence on …

We devise a framework for computing an approximate solution path for an important class of
parameterized semidefinite problems that is guaranteed to be ε-close to the exact solution …

Unconstrained optimization problems are typically solved using iterative methods, which
often depend on line search techniques to determine optimal step lengths in each iteration …