Graph-Based Correlation Matrix Generation: A Convex Optimization Approach
This work addresses the generation of theoretical correlation matrices with prescribed sparsity patterns associated to graph structures. We propose a novel convex optimization framework in which an initial matrix is projected onto an elliptope under a positive semidefiniteness constraint. Several numerical schemes are implemented and compared. The problem falls within the broader class of matrix completion, where off-diagonal entries corresponding to absent edges are fixed to zero and diagonal entries are fixed to one. Beyond this structural constraint, the approach offers greater flexibility than existing methods by allowing control over the mean of the off-diagonal entry distribution, e...
| Authors | Ali Fakhar, K{é}vin Polisano, Ir{è}ne Gannaz et al. |
|---|---|
| Primary category | stat.ML |
| Published | 24 July 2026 |
| Last updated at source | 24 July 2026 |
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