A globally convergent fast iterative shrinkage-thresholding algorithm with a new momentum factor for single and multi-objective convex optimization. (arXiv:2205.05262v1 [math.OC])

Convex-composite optimization, which minimizes an objective function
represented by the sum of a differentiable function and a convex one, is widely
used in machine learning and signal/image processing. Fast Iterative Shrinkage
Thresholding Algorithm (FISTA) is a typical method for solving this problem and
has a global convergence rate of $O(1 / k^2)$. Recently, this has been extended
to multi-objective optimization, together with the proof of the $O(1 / k^2)$
global convergence rate. However, its momentum factor is classical, and the …

algorithm arxiv iterative math optimization shrinkage thresholding