A compact row storage scheme for Cholesky factors using elimination trees
ACM Transactions on Mathematical Software (TOMS)
The role of elimination trees in sparse factorization
SIAM Journal on Matrix Analysis and Applications
A generalized envelope method for sparse factorization by rows
ACM Transactions on Mathematical Software (TOMS)
ACM Transactions on Mathematical Software (TOMS)
Sparse matrices in matlab: design and implementation
SIAM Journal on Matrix Analysis and Applications
A supernodal Cholesky factorization algorithm for shared-memory multiprocessors
SIAM Journal on Scientific Computing
An Efficient Algorithm to Compute Row and Column Counts for Sparse Cholesky Factorization
SIAM Journal on Matrix Analysis and Applications
An Approximate Minimum Degree Ordering Algorithm
SIAM Journal on Matrix Analysis and Applications
The Design of a User Interface for a Sparse Matrix Package
ACM Transactions on Mathematical Software (TOMS)
Computer Solution of Large Sparse Positive Definite
Computer Solution of Large Sparse Positive Definite
Algorithm 837: AMD, an approximate minimum degree ordering algorithm
ACM Transactions on Mathematical Software (TOMS)
Square Root SAM: Simultaneous Localization and Mapping via Square Root Information Smoothing
International Journal of Robotics Research
Nanofiber textiles -problem of FEM modelling the coupled heat and moisture transfer
SMO'06 Proceedings of the 6th WSEAS International Conference on Simulation, Modelling and Optimization
Algorithm 887: CHOLMOD, Supernodal Sparse Cholesky Factorization and Update/Downdate
ACM Transactions on Mathematical Software (TOMS)
SBA: A software package for generic sparse bundle adjustment
ACM Transactions on Mathematical Software (TOMS)
Covariance recovery from a square root information matrix for data association
Robotics and Autonomous Systems
Probabilistic structure matching for visual SLAM with a multi-camera rig
Computer Vision and Image Understanding
Study of parameterizations for the rigid body transformations of the scan registration problem
Computer Vision and Image Understanding
Sparse non-linear least squares optimization for geometric vision
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
The university of Florida sparse matrix collection
ACM Transactions on Mathematical Software (TOMS)
Solving Rational Eigenvalue Problems via Linearization
SIAM Journal on Matrix Analysis and Applications
GPstuff: Bayesian modeling with Gaussian processes
The Journal of Machine Learning Research
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The LDL software package is a set of short, concise routines for factorizing symmetric positive-definite sparse matrices, with some applicability to symmetric indefinite matrices. Its primary purpose is to illustrate much of the basic theory of sparse matrix algorithms in as concise a code as possible, including an elegant method of sparse symmetric factorization that computes the factorization row-by-row but stores it column-by-column. The entire symbolic and numeric factorization consists of less than 50 executable lines of code. The package is written in C, and includes a MATLAB interface.