A novel algorithm is developed for the problem of finding a low-rank correlation matrix nearest to a given correlation … matrix. The algorithm is based on majorization and, therefore, it is globally convergent. The algorithm is computationally … simulation study suggests that majorization compares favourably with competing approaches in terms of the quality of the solution …

In this paper a novel method is developed for the problem of finding a low-rank correlation matrix nearest to a given … correlation matrix. The method is based on majorization and therefore it is globally convergent. The method is computationally … simulation study suggests that majorization compares favourably with competing approaches in terms of the quality of the solution …

Geometric optimisation algorithms are developed that efficiently find the nearest low-rank correlation matrix. We show … nearest low-rank correlation matrix occurs as part of the calibration of multi-factor interest rate market models to …

Geometric optimisation algorithms are developed that efficiently find the nearest low-rank correlation matrix. We show … nearest low-rank correlation matrix occurs as part of the calibration of multi-factor interest rate market models to …

majorization, hypersphere decomposition, semi-definite programming, or geometric programming, etc. In this paper we propose to …

Geometric optimisation algorithms are developed that efficiently find the nearest low-rank correlation matrix. We show … nearest low-rank correlation matrix occurs as part of the calibration of multi-factor interest rate market models to …