| Abstract: |
| Large-scale imaging inverse problems are often limited by two expensive operations repeated at every iteration: gradient updates for the data-fidelity term and proximal evaluations for the regulariser. In this talk, we show how randomized proximal skipping and data splitting can substantially reduce this cost without sacrificing reconstruction quality. Numerical results on synthetic and real tomographic datasets demonstrate substantial runtime reductions, with speed-ups of 5x to 20x compared with standard approaches, while maintaining high-quality reconstructions. |
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