3 citations · 3 across the 6 of their papers we have counts for
5 papers · 1 filter
Bundle Network: a Machine Learning-Based Bundle Method
Francesca Demelas, Joseph Le Roux, Antonio Frangioni +3
This paper presents Bundle Network, a learning-based algorithm inspired by the Bundle Method for convex non-smooth minimization problems. Unlike classical approaches that rely on h…
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…
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…
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…
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…