Results 41 to 50 of about 510,427 (158)
Developing robust incomplete Cholesky factorizations in half precision arithmetic
Numerical AlgorithmsAbstract Incomplete factorizations have long been popular general-purpose algebraic preconditioners for solving large sparse linear systems of equations. Guaranteeing the factorization is breakdown free while computing a high quality preconditioner is challenging.
Jennifer A. Scott, Miroslav Tuma
openaire +2 more sources
Why Do Hedgers Hedge? The Role of Ambiguity
Journal of Futures Markets, Volume 46, Issue 6, Page 1053-1078, June 2026.ABSTRACT This paper investigates whether ambiguity influences hedging behavior in commodity futures markets. Using high‐frequency crude oil futures data, distinct measures of risk and ambiguity are linked to weekly hedging positions from the Commodity Futures Trading Commission (CFTC).
Fiona Höllmann
wiley +1 more source
Incomplete Cholesky factorization in fixed memory with flexible drop-tolerance strategy
Second IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, 2003. Proceedings, 2004 We propose an incomplete Cholesky factorization for the solution of large positive definite systems of equations and for the solution of large-scale trust region sub-problems. The factorization is based on the two- parameter (m, p) drop-tolerance strategy for insignificant elements in the incomplete factor matrix. The factorization proposed essentially
openaire +2 more sources
CPT: Pharmacometrics &Systems Pharmacology, Volume 15, Issue 6, June 2026.ABSTRACT Mixed effects models are a backbone of pharmacometrics, and NONMEM software, with the first‐order conditional method with interaction having become the de facto industry standard for model estimation. Documentation exists for the general mathematical methodology for estimation, but many technical and implementation details are lacking. OpenPMX
Douglas J. Eleveld +4 more
wiley +1 more source
Accelerating Conjugate Gradient Solvers for Homogenization Problems With Unitary Neural Operators
International Journal for Numerical Methods in Engineering, Volume 127, Issue 5, 15 March 2026.ABSTRACT Rapid and reliable solvers for parametric partial differential equations (PDEs) are needed in many scientific and engineering disciplines. For example, there is a growing demand for composites and architected materials with heterogeneous microstructures.
Julius Herb, Felix Fritzen
wiley +1 more source
The cholesky factorization in interior point methods [PDF]
, 2005 The paper concerns the Cholesky factorization of symmetric positive definite matrices arising in interior point methods. Our investigation is based on a property of the Cholesky factorization which interprets “small” diagonal values during factorization ...
Mészáros, C.
core +1 more source
A Comparison of Monte Carlo Based Marginal Likelihood Estimators
WIREs Computational Statistics, Volume 18, Issue 1, March 2026.Comparison of marginal likelihood estimation methods. ABSTRACT Marginal likelihood plays a central role in Bayesian model comparison and hypothesis testing, but its computation is often challenging in practice. This article reviews recent Monte Carlo methods that rely on the availability of Markov chain Monte Carlo (MCMC) samples from the posterior and
Aolan Li +5 more
wiley +1 more source
Modifying a Sparse Cholesky Factorization
, 1999 Given a sparse symmetric positive de nite matrix AA and an associated sparse Cholesky factorization LDL , we develop sparse techniques for obtaining the new factorization associated with either adding a column to A or deleting a column from A ...
Cholesky Factorization Ldl +3 more
core
A Jacobian‐Free Newton‐Krylov Method for Cell‐Centred Finite Volume Solid Mechanics
International Journal for Numerical Methods in Engineering, Volume 127, Issue 3, 15 February 2026.ABSTRACT This study proposes a Jacobian‐free Newton‐Krylov approach for finite‐volume solid mechanics. Traditional Newton‐based approaches require explicit Jacobian matrix formation and storage, which can be computationally expensive and memory‐intensive.
Philip Cardiff +3 more
wiley +1 more source
A Preconditioner for Solving Linear Programming Problems With Dense Columns
Numerical Linear Algebra with Applications, Volume 33, Issue 1, February 2026.ABSTRACT The Interior‐Point Methods are a class for solving linear programming problems that rely upon the solution of linear systems. At each iteration, it becomes important to determine how to solve these linear systems when the constraint matrix of the linear programming problem includes dense columns.
Catalina J. Villalba +1 more
wiley +1 more source