336. Incorporating History and Deviations in Forward-Backward Splitting
Invited abstract in session WC-5: Optimization for learning I, stream Optimization for learning.
Wednesday, 10:05 - 11:20Room: M:N
Authors (first author is the speaker)
| 1. | Pontus Giselsson
|
| Dept. of Automatic Control, Lund University |
Abstract
We propose a variation of the forward-backward splitting method for solving structured monotone inclusions. Our method integrates past iterates and two deviation vectors into the update equations. These deviation vectors bring flexibility to the algorithm and can be chosen arbitrarily as long as they together satisfy a norm condition. We present special cases where the deviation vectors, selected as predetermined linear combinations of previous iterates, always meet the norm condition. Notably, we introduce an algorithm employing a scalar parameter to interpolate between the conventional forward--backward splitting scheme and an accelerated O(1/n^2)-convergent forward-backward method that encompasses both the accelerated proximal point method and the Halpern iteration as special cases. The existing methods correspond to the two extremes of the allowed scalar parameter range. By choosing the interpolation scalar near the midpoint of the permissible range, our algorithm significantly outperforms these previously known methods when addressing a basic monotone inclusion problem stemming from minimax optimization.
Keywords
- Large- and Huge-scale optimization
Status: accepted
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