Generating Correlation Matrices with Graph Structures Using Convex Optimization
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866910843390656512 |
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| author | Fakhar, Ali Polisano, Kévin Gannaz, Irène Achard, Sophie |
| author_facet | Fakhar, Ali Polisano, Kévin Gannaz, Irène Achard, Sophie |
| contents | This work deals with the generation of theoretical correlation matrices with specific sparsity patterns, associated to graph structures. We present a novel approach based on convex optimization, offering greater flexibility compared to existing techniques, notably by controlling the mean of the entry distribution in the generated correlation matrices. This allows for the generation of correlation matrices that better represent realistic data and can be used to benchmark statistical methods for graph inference. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_17981 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generating Correlation Matrices with Graph Structures Using Convex Optimization Fakhar, Ali Polisano, Kévin Gannaz, Irène Achard, Sophie Signal Processing Optimization and Control Statistics Theory Methodology This work deals with the generation of theoretical correlation matrices with specific sparsity patterns, associated to graph structures. We present a novel approach based on convex optimization, offering greater flexibility compared to existing techniques, notably by controlling the mean of the entry distribution in the generated correlation matrices. This allows for the generation of correlation matrices that better represent realistic data and can be used to benchmark statistical methods for graph inference. |
| title | Generating Correlation Matrices with Graph Structures Using Convex Optimization |
| topic | Signal Processing Optimization and Control Statistics Theory Methodology |
| url | https://arxiv.org/abs/2502.17981 |