On the convexity of bit depth allocation for linear MMSE estimation in wireless sensor networks KU Leuven
SuperMann: A Superlinearly Convergent Algorithm for Finding Fixed Points of Nonexpansive Operators KU Leuven
Nonlinear Model Predictive Control for Distributed Motion Planning in Road Intersections Using PANOC KU Leuven
Safe Learning-Based Control of Stochastic Jump Linear Systems: a Distributionally Robust Approach KU Leuven
We propose a Forward-Backward Truncated-Newton method (FBTN) for minimizing the sum of two convex functions, one of which smooth. Unlike other proximal Newton methods, our approach does not involve the employment of variable metrics, but is rather based on a reformulation of the original problem as the unconstrained minimization of a continuously differentiable function, the forward-backward envelope (FBE). We introduce a generalized Hessian for ...
A New Randomized Block-Coordinate Primal-Dual Proximal Algorithm for Distributed Optimization KU Leuven
This paper proposes TriPD, a new primal-dual algorithm for minimizing the sum of a Lipschitz-differentiable convex function and two possibly nonsmooth convex functions, one of which is composed with a linear mapping. We devise a randomized block-coordinate version of the algorithm which converges under the same stepsize conditions as the full algorithm. It is shown that both the original as well as the block-coordinate scheme feature linear ...