5 citations · 8 across the 9 of their papers we have counts for
10 papers
An Integer Programming Approach to Compute Lower Bounds for Ramsey Numbers Using Circulant Graphs
Stefano Coniglio, Fabio Furini, Ivana Ljubić +3
The Ramsey number is the smallest order at which every red-blue edge coloring of a complete graph must contain a blue clique (a complete subgraph) of size or a red cli…
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…
Alleviating the quantum Big- problem
Edoardo Alessandroni, Sergi Ramos-Calderer, Ingo Roth +2
A major obstacle for quantum optimizers is the reformulation of constraints as a quadratic unconstrained binary optimization (QUBO). Current QUBO translators exaggerate the weight…
Deep Reinforcement Learning for Combined Coverage and Resource Allocation in UAV-aided RAN-slicing
Lorenzo Bellone, Boris Galkin, Emiliano Traversi +1
Network slicing is a well assessed approach enabling virtualization of the mobile core and radio access network (RAN) in the emerging 5th Generation New Radio. Slicing is of paramo…
Branch and Price for Submodular Bin Packing
Liding Xu, Claudia D'Ambrosio, Sonia Haddad Vanier +1
The Submodular Bin Packing (SMBP) problem asks for packing unsplittable items into a minimal number of bins for which the capacity utilization function is submodular. SMBP is equiv…
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…
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…