Accelerated Proximal Gradient Methods in the affine-quadratic case: Strong convergence and limit identification
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| Format: | Preprint |
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2025
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| author | Moursi, Walaa M. Naguib, Andrew Pavlovic, Viktor Vavasis, Stephen A. |
| author_facet | Moursi, Walaa M. Naguib, Andrew Pavlovic, Viktor Vavasis, Stephen A. |
| contents | Recent works by Bot-Fadili-Nguyen (arXiv:2510.22715) and by Jang-Ryu (arXiv:2510.23513) resolve long-standing iterate convergence questions for accelerated (proximal) gradient methods. In particular, Bot-Fadili-Nguyen prove weak convergence of discrete accelerated gradient descent (AGD) iterates and, crucially, convergence of the accelerated proximal gradient (APG) method in the composite setting, with extensions to infinite-dimensional Hilbert spaces. In parallel, Jang-Ryu establish point convergence for the continuous-time accelerated flow and for discrete AGD in finite dimensions.
These results leave open which minimizer is selected by the iterates. We answer this in the affine-quadratic setting: when initialized at the same point, the difference between the proximal gradient (PGM) and APG iterates converges weakly to zero. Consequently, APG converges weakly to the best approximation of the initial point in the solution set. Moreover, under mild assumptions on the parameter sequence, we obtain strong convergence of APG. The result is tight: a two-dimensional example shows that coincidence of the APG and PGM limits is specific to the affine-quadratic regime and does not hold in general. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_06560 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Accelerated Proximal Gradient Methods in the affine-quadratic case: Strong convergence and limit identification Moursi, Walaa M. Naguib, Andrew Pavlovic, Viktor Vavasis, Stephen A. Optimization and Control Functional Analysis Primary 90C25, 65K05, Secondary 49M27 Recent works by Bot-Fadili-Nguyen (arXiv:2510.22715) and by Jang-Ryu (arXiv:2510.23513) resolve long-standing iterate convergence questions for accelerated (proximal) gradient methods. In particular, Bot-Fadili-Nguyen prove weak convergence of discrete accelerated gradient descent (AGD) iterates and, crucially, convergence of the accelerated proximal gradient (APG) method in the composite setting, with extensions to infinite-dimensional Hilbert spaces. In parallel, Jang-Ryu establish point convergence for the continuous-time accelerated flow and for discrete AGD in finite dimensions. These results leave open which minimizer is selected by the iterates. We answer this in the affine-quadratic setting: when initialized at the same point, the difference between the proximal gradient (PGM) and APG iterates converges weakly to zero. Consequently, APG converges weakly to the best approximation of the initial point in the solution set. Moreover, under mild assumptions on the parameter sequence, we obtain strong convergence of APG. The result is tight: a two-dimensional example shows that coincidence of the APG and PGM limits is specific to the affine-quadratic regime and does not hold in general. |
| title | Accelerated Proximal Gradient Methods in the affine-quadratic case: Strong convergence and limit identification |
| topic | Optimization and Control Functional Analysis Primary 90C25, 65K05, Secondary 49M27 |
| url | https://arxiv.org/abs/2511.06560 |