1990SIAM Journal on Scientific and Statistical ComputingRequires access

A New Modified Cholesky Factorization

Robert B. Schnabel, Elizabeth Eskow

Open publisher page 163 citations

Abstract

The modified Cholesky factorization of Gill and Murray plays an important role in optimization algorithms. Given a symmetric but not necessarily positive-definite matrix A, it computes a Cholesky factorization of $A + E$, where $E = 0$ if A is safely positive-definite, and E is a diagonal matrix chosen to make $A + E$ positive-definite otherwise. The factorization costs only a small multiple of $n^2 $ operations more than the standard Cholesky factorization. A new algorithm that has these same properties, but for which the theoretical bound on $||E||_\infty $ is substantially smaller, is presented. It is based upon two new techniques, the use of Gerschgorin bounds in selecting the elements of E, and a new way of monitoring positive definiteness. In extensive computational tests on indefinite matrices, the new factorization virtually always produces smaller values of $||E||_\infty $ than the existing method, without impairing the conditioning of $A + E$. In some cases the improvements are substantial. The new factorization has already been useful in optimization algorithms.

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What this paper is about

The modified Cholesky factorization of Gill and Murray plays an important role in optimization algorithms. Given a symmetric but not necessarily positive-definite matrix A, it computes a Cholesky factorization of $A + E$, where $E = 0$ if A is safely positive-definite, and E is a diagonal matrix chosen to make $A + E$ positive-definite otherwise. The factorization costs only a small multiple of $n^2 $ operations more than the standard Cholesky factorization. A new algorithm that has these same properties, but for which the theoretical bound on $||E||_\infty $ is substantially smaller, is presented. It is based upon two new techniques, the use of Gerschgorin bounds in selecting the elements of E, and a new way of monitoring positive definiteness. In extensive computational tests on indefinite matrices, the new factorization virtually always produces smaller values of $||E||_\infty $ than the existing method, without impairing the conditioning of $A + E$. In some cases the improvements are substantial. The new factorization has already been useful in optimization algorithms.

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Available abstract

The modified Cholesky factorization of Gill and Murray plays an important role in optimization algorithms. Given a symmetric but not necessarily positive-definite matrix A, it computes a Cholesky factorization of $A + E$, where $E = 0$ if A is safely positive-definite, and E is a diagonal matrix chosen to make $A + E$ positive-definite otherwise. The factorization costs only a small multiple of $n^2 $ operations more than the standard Cholesky factorization. A new algorithm that has these same properties, but for which the theoretical bound on $||E||_\infty $ is substantially smaller, is presented. It is based upon two new techniques, the use of Gerschgorin bounds in selecting the elements of E, and a new way of monitoring positive definiteness. In extensive computational tests on indefinite matrices, the new factorization virtually always produces smaller values of $||E||_\infty $ than the existing method, without impairing the conditioning of $A + E$. In some cases the improvements are substantial. The new factorization has already been useful in optimization algorithms.

Key concepts: Cholesky decomposition, Incomplete Cholesky factorization, Minimum degree algorithm, Factorization, Positive-definite matrix, Positive definiteness, Dixon's factorization method, Mathematics

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