LDUFacts

Introduction

This package is dedicated to the L-D-Lt factorization of symmetric real and Hermitian matrices.

In contrast to Cholesky factorization, which is appropriate for most use cases, it has the following features:

• handle also the indefinite case (Cholesky requires positive (semi)-definite)
• square-root free algorithm
• exact operation with real and complex rational types
• exact operation with user-defined field types
• option of full pivot search

Algorithm

Factors P, L, D of matrix A are found in a way, that P' * A * P = L * D * L'.

Here D is diagonal and real, L is lower unit triangular, and P is a 'simple' square matrix. With diagonal pivot search, P is a permutation matrix, with full pivot search up to n-1 nonzero additional matrix elements may occur. Without pivot search, P is the unit matrix.

When not a full pivot search is done, it is possible, that the algorithm fails before the factors are found.

For positive definite matrices, the no-pivot version is guaranteed to succeed. For positive semi-definite matrices, a diagonal pivot search always succeeds, for infefinite matrices, a full pivot search is required for that.

There are examples of regular matrices, (e.g. [0. 1; 1 0]), which do not have any factorization where P is a permutation. For those cases the full pivot search is required.

Usage

]add LDUFacts

using LDUFacts

A = Matrix(Symmetric(rand(5, 5)))
la = ldu(A, FullPivot())

issuccess(la)
rank(la)
la.L
la.d
la.P' * A * la.P - la.L * la.D * la.L' # should be close to zero