activity
20172022
most citedDeep Reinforcement Learning for Combined Coverage and Resource Allocation in UAV-aided RAN-slicing

3 citations · 3 across the 5 of their papers we have counts for

collaborators

5 papers

cs.RO2021

A Semidefinite Optimization-based Branch-and-Bound Algorithm for Several Reactive Optimal Power Flow Problems

Julie Sliwak, Miguel Anjos, Lucas Létocart +1

The Reactive Optimal Power Flow (ROPF) problem consists in computing an optimal power generation dispatch for an alternating current transmission network that respects power flow e…

math.OC2019

Improving Clique Decompositions of Semidefinite Relaxations for Optimal Power Flow Problems

Julie Sliwak, Miguel Anjos, Lucas Létocart +2

Semidefinite Programming (SDP) provides tight lower bounds for Optimal Power Flow problems. However, solving large-scale SDP problems requires exploiting sparsity. In this paper, w…

math.OC2019

A Julia Module for Polynomial Optimization with Complex Variables applied to Optimal Power Flow

Julie Sliwak, Manuel Ruiz, Miguel F. Anjos +2

Many optimization problems in power transmission networks can be formulated as polynomial problems with complex variables. A polynomial optimization problem with complex variables…

math.OC2018

Optimal Solution of Vehicle Routing Problems with Fractional Objective Function

Roberto Baldacci, Andrew Lim, Emiliano Traversi +1

This work proposes a first extensive analysis of the Vehicle Routing Problem with Fractional Objective Function (vrpfof). We investigate how the principal techniques used either in…

math.OC2017

A simplicial decomposition framework for large scale convex quadratic programming

Enrico Bettiol, Lucas Létocart, Francesco Rinaldi +1

In this paper, we analyze in depth a simplicial decomposition like algorithmic framework for large scale convex quadratic programming. In particular, we first propose two tailored…