# An efficient and robust SAV based algorithm for discrete gradient systems arising from optimizations

arXiv: https://arxiv.org/abs/2301.02942 · HTML: https://mlsanity.com/paper/2301.02942/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jan 2023.

## Built on by (most cited first, 1 of 1)

- [An Element-wise RSAV Algorithm for Unconstrained Optimization Problems](https://mlsanity.com/paper/2309.04013/) (2023)

## Abstract (arXiv)

We propose in this paper a new minimization algorithm based on a slightly modified version of the scalar auxiliary variable (SAV) approach coupled with a relaxation step and an adaptive strategy. It enjoys several distinct advantages over popular gradient based methods: (i) it is unconditionally energy diminishing with a modified energy which is intrinsically related to the original energy, thus no parameter tuning is needed for stability; (ii) it allows the use of large step-sizes which can effectively accelerate the convergence rate. We also present a convergence analysis for some SAV based algorithms, which include our new algorithm without the relaxation step as a special case. We apply our new algorithm to several illustrative and benchmark problems, and compare its performance with several popular gradient based methods. The numerical results indicate that the new algorithm is very robust, and its adaptive version usually converges significantly faster than those popular gradient descent based methods.