Positive-definite "approximations" to matrices
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January 2016


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PositiveFactorizations is a package for computing a positive definite matrix decomposition (factorization) from an arbitrary symmetric input. The motivating application is optimization (Newton or quasi-Newton methods), in which the canonical search direction -H\g (H being the Hessian and g the gradient) may not be a descent direction if H is not positive definite. This package provides an efficient approach to computing -Htilde\g, where Htilde is equal to H if H is positive definite, and otherwise is a positive definite matrix that is "spiritually like H."

The approach favored here is different from the well-known Gill-Murray-Wright approach of computing the Cholesky factorization of H+E, where E is a minimal correction needed to make H+E positive-definite (sometimes known as GMW81). See the discussion starting here for justification; briefly, the idea of a small correction conflates large negative eigenvalues with numerical roundoff error, which (when stated that way) seems like a puzzling choice. In contrast, this package provides methods that are largely equivalent to taking the absolute value of the diagonals D in an LDLT factorization, and setting any "tiny" diagonals (those consistent with roundoff error) to 1. For a diagonal matrix with some entries negative, this results in approximately twice the correction used in GMW81.


Given a symmetric matrix H, compute a positive factorization F like this:

F = cholesky(Positive, H, [pivot=Val{false}])

Pivoting (turned on with Val{true}) can make the correction smaller and increase accuracy, but is not necessary for existence or stability.

For a little more information, call ldlt instead:

F, d = ldlt(Positive, H, [pivot=Val{false}])

F will be the same as for cholesky, but this also returns d, a vector of Int8 with values +1, 0, or -1 indicating the sign of the diagonal as encountered during processing (so in order of rows/columns if not using pivoting, in order of pivot if using pivoting). This output can be useful for determining whether the original matrix was already positive (semi)definite.

Note that cholesky/ldlt can be used with any matrix, even those which lack a conventional LDLT factorization. For example, the matrix [0 1; 1 0] is factored as L = [1 0; 0 1] (the identity matrix), with all entries of d being 0. Symmetry is assumed but not checked; only the lower-triangle of the input is used.

cholesky is recommended because it is very efficient. A slower alternative is to use eigen:

F = eigen(Positive, H)

which may be easier to reason about from the standpoint of fundamental linear algebra.