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 …

We present a novel data structure, the Bayes tree, that provides an algorithmic foundation
enabling a better understanding of existing graphical model inference algorithms and their …

We describe the University of Florida Sparse Matrix Collection, a large and actively growing
set of sparse matrices that arise in real applications. The Collection is widely used by the …

In this paper, we present incremental smoothing and map** (iSAM), which is a novel
approach to the simultaneous localization and map** problem that is based on fast …

Solving the SLAM (simultaneous localization and map**) problem is one way to enable a
robot to explore, map, and navigate in a previously unknown environment. Smoothing …

CHOLMOD is a set of routines for factorizing sparse symmetric positive definite matrices of
the form A or AA T, updating/downdating a sparse Cholesky factorization, solving linear …

We investigate several ways to improve the performance of sparse LU factorization with
partial pivoting, as used to solve unsymmetric linear systems. We introduce the notion of …

We give an overview of the algorithms, design philosophy, and implementation techniques
in the software SuperLU, for solving sparse unsymmetric linear systems. In particular, we …

We present the main algorithmic features in the software package SuperLU_DIST, a
distributed-memory sparse direct solver for large sets of linear equations. We give in detail …