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Incidence matrix
Related publications (35)
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Reactive power optimization of distribution networks is traditionally addressed by physical model based methods, which often lead to locally optimal solutions and require heavy online inference time consumption. To improve the quality of the solution and r ...
State Grid Electric Power Research Inst2024Dispatch-aware Optimal Planning of Active Distribution Networks including Energy Storage Systems
The thesis develops a planning framework for ADNs to achieve their dispatchability by means of ESS allocation while ensuring a reliable and secure operation of ADNs. Second, the framework is extended to include grid reinforcements and ESSs planning. Finall ...
EPFL2023Spectral Hypergraph Sparsifiers of Nearly Linear Size
Mikhail Kapralov, Jakab Tardos
Graph sparsification has been studied extensively over the past two decades, culminating in spectral sparsifiers of optimal size (up to constant factors). Spectral hypergraph sparsification is a natural analogue of this problem, for which optimal bounds on ...
IEEE COMPUTER SOC2022Multilayer Graph Clustering With Optimized Node Embedding
Pascal Frossard, Mireille El Gheche
We are interested in multilayer graph clustering, which aims at dividing the graph nodes into categories or communities. To do so, we propose to learn a clustering-friendly embedding of the graph nodes by solving an optimization problem that involves a fid ...
IEEE2021Towards Tight Bounds for Spectral Sparsification of Hypergraphs
Mikhail Kapralov, Jakab Tardos
Cut and spectral sparsification of graphs have numerous applications, including e.g. speeding up algorithms for cuts and Laplacian solvers. These powerful notions have recently been extended to hypergraphs, which are much richer and may offer new applicati ...
ASSOC COMPUTING MACHINERY2021Online Distributed Learning Over Graphs With Multitask Graph-Filter Models
In this article, we are interested in adaptive and distributed estimation of graph filters from streaming data. We formulate this problem as a consensus estimation problem over graphs, which can be addressed with diffusion LMS strategies. Most popular grap ...
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020Graph Laplacians for Rotation Equivariant Convolutional Neural Networks
A fundamental problem in signal processing is to design computationally efficient algorithms to filter signals. In many applications, the signals to filter lie on a sphere. Meaningful examples of data of this kind are weather data on the Earth, or images o ...
2019GOT: An Optimal Transport framework for Graph comparison
Pascal Frossard, Mireille El Gheche, Hermina Petric Maretic, Giovanni Chierchia
We present a novel framework based on optimal transport for the challenging problem of comparing graphs. Specifically, we exploit the probabilistic distribution of smooth graph signals defined with respect to the graph topology. This allows us to derive an ...
NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2019A Preconditioned Graph Diffusion LMS for Adaptive Graph Signal Processing
Graph filters, defined as polynomial functions of a graph-shift operator (GSO), play a key role in signal processing over graphs. In this work, we are interested in the adaptive and distributed estimation of graph filter coefficients from streaming graph s ...
IEEE COMPUTER SOC2018Extension complexity of stable set polytopes of bipartite graphs
Yuri Faenza, Manuel Francesco Aprile
The extension complexity xc(P) of a polytope P is the minimum number of facets of a polytope that affinely projects to P. Let G be a bipartite graph with n vertices, m edges, and no isolated vertices. Let STAB(G) be the convex hull of the stable sets of G. ...
Springer2017