activity
20162026
most citedEmbedded nonlinear model predictive control for obstacle avoidance using PANOC

100 citations · 192 across the 70 of their papers we have counts for

collaborators
Showing 2016Show all

6 papers · 1 filter

math.OC2016★ 1 cited

Stochastic economic model predictive control for Markovian switching systems

Pantelis Sopasakis, Domagoj Herceg, Panagiotis Patrinos +1

The optimization of process economics within the model predictive control (MPC) formulation has given rise to a new control paradigm known as economic MPC (EMPC). Several authors h…

math.OC2016

SuperMann: a superlinearly convergent algorithm for finding fixed points of nonexpansive operators

Andreas Themelis, Panagiotis Patrinos

Operator splitting techniques have recently gained popularity in convex optimization problems arising in various control fields. Being fixed-point iterations of nonexpansive operat…

math.OC2016

Forward-backward envelope for the sum of two nonconvex functions: Further properties and nonmonotone line-search algorithms

Andreas Themelis, Lorenzo Stella, Panagiotis Patrinos

We propose ZeroFPR, a nonmonotone linesearch algorithm for minimizing the sum of two nonconvex functions, one of which is smooth and the other possibly nonsmooth. ZeroFPR is the fi…

math.OC2016

Accelerated reconstruction of a compressively sampled data stream

Pantelis Sopasakis, Nikolaos Freris, Panagiotis Patrinos

The traditional compressed sensing approach is naturally offline, in that it amounts to sparsely sampling and reconstructing a given dataset. Recently, an online algorithm for perf…

math.OC2016

Forward-backward quasi-Newton methods for nonsmooth optimization problems

Lorenzo Stella, Andreas Themelis, Panagiotis Patrinos

The forward-backward splitting method (FBS) for minimizing a nonsmooth composite function can be interpreted as a (variable-metric) gradient method over a continuously differentiab…

math.OC2016

New Primal-Dual Proximal Algorithm for Distributed Optimization

Puya Latafat, Lorenzo Stella, Panagiotis Patrinos

We consider a network of agents, each with its own private cost consisting of the sum of two possibly nonsmooth convex functions, one of which is composed with a linear operator. A…