Summary The SuiteSparse Matrix Collection (formerly known as the University of Florida
Sparse Matrix Collection)(Davis & Hu, 2011) has grown significantly since its introduction …

The correlation clustering problem identifies clusters in a set of objects when the qualitative
information about objects' mutual similarities or dissimilarities is given in a signed network …

Motivated by applications in community detection and dense subgraph discovery, we
consider new clustering objectives in hypergraphs and bipartite graphs. These objectives …

Given a square matrix with noisy dissimilarity measures between pairs of data samples, the
metric nearness model computes the best approximation of the matrix from a set of valid …

Graph clustering is a fundamental task in network analysis where the goal is to detect sets of
nodes that are well-connected to each other but sparsely connected to the rest of the graph …

Max-k-Cut and correlation clustering are fundamental graph partitioning problems. For a
graph $ G=(V, E) $ with $ n $ vertices, the methods with the best approximation guarantees …

Finding clusters of well-connected nodes in a graph is an extensively studied problem in
graph-based data analysis. Because of its many applications, a large number of distinct …

Many important machine learning problems can be formulated as highly constrained convex
optimization problems. One important example is metric constrained problems. In this paper …

Many clustering applications in machine learning and data mining rely on solving metric-
constrained optimization problems. These problems are characterized by O (n 3) constraints …