An Element-wise RSAV Algorithm for Unconstrained Optimization Problems

09/07/2023
by   Shiheng Zhang, et al.
0

We present a novel optimization algorithm, element-wise relaxed scalar auxiliary variable (E-RSAV), that satisfies an unconditional energy dissipation law and exhibits improved alignment between the modified and the original energy. Our algorithm features rigorous proofs of linear convergence in the convex setting. Furthermore, we present a simple accelerated algorithm that improves the linear convergence rate to super-linear in the univariate case. We also propose an adaptive version of E-RSAV with Steffensen step size. We validate the robustness and fast convergence of our algorithm through ample numerical experiments.

READ FULL TEXT
research
10/14/2021

Additive Schwarz Methods for Convex Optimization with Backtracking

This paper presents a novel backtracking strategy for additive Schwarz m...
research
11/14/2017

A Robust Variable Step Size Fractional Least Mean Square (RVSS-FLMS) Algorithm

In this paper, we propose an adaptive framework for the variable step si...
research
02/23/2023

A subgradient method with constant step-size for ℓ_1-composite optimization

Subgradient methods are the natural extension to the non-smooth case of ...
research
08/31/2020

Super-linear convergence in the p-adic QR-algorithm

The QR-algorithm is one of the most important algorithms in linear algeb...
research
04/14/2021

Improving the Accuracy and Consistency of the Scalar Auxiliary Variable (SAV) Method with Relaxation

The scalar auxiliary variable (SAV) method was introduced by Shen et al....
research
01/07/2023

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

We propose in this paper a new minimization algorithm based on a slightl...
research
05/04/2023

Variations on a Theme by Blahut and Arimoto

The Blahut-Arimoto (BA) algorithm has played a fundamental role in the n...

Please sign up or login with your details

Forgot password? Click here to reset