We review the use of factor graphs for the modeling and solving of large-scale inference
problems in robotics. Factor graphs are a family of probabilistic graphical models, other …

Wilkinson defined a sparse matrix as one with enough zeros that it pays to take advantage of
them. 1 This informal yet practical definition captures the essence of the goal of direct …

Graph embedding methods produce unsupervised node features from graphs that can then
be used for a variety of machine learning tasks. However, modern graph datasets contain …

Recently, a number of researchers have investigated a class of graph partitioning algorithms
that reduce the size of the graph by collapsing vertices and edges, partition the smaller …

In this paper, we analyze the main features and discuss the tuning of the algorithms for the
direct solution of sparse linear systems on distributed memory computers developed in the …

This book presents the fundamentals of sparse matrix algorithms, from theory to algorithms
and data structures to working code. The focus is on direct methods for solving systems of …

Sparsity of representations of signals has been shown to be a key concept of fundamental
importance in fields such as blind source separation, compression, sampling and signal …

In many large scale scientific or engineering computations, ranging from computing the
frequency response of a circuit to the earthquake response of a buildingto the energy levels …

The subject of sparse matrices has its root in such diverse fields as management science,
power systems analysis, surveying, circuit theory, and structural analysis. Efficient use of …

Graphs are often used to encapsulate the relationship between objects. Graph drawing
enables visualization of these relationships. The usefulness of the representation is …