# An efficient gradient-based algorithm with descent direction for unconstrained optimization with applications to image restoration and robotic motion control

## TL;DR

This paper introduces a new gradient-based algorithm that improves optimization for image restoration and robotic motion control.

## Contribution

A modified conjugate gradient method with guaranteed descent direction and global convergence is proposed.

## Key findings

- The algorithm achieves high precision in restoring corrupted images.
- It effectively manages motion control in a 3DOF robotic arm model.
- The method shows superior performance across various test problems.

## Abstract

This study presents a novel gradient-based algorithm designed to enhance the performance of optimization models, particularly in computer science applications such as image restoration and robotic motion control. The proposed algorithm introduces a modified conjugate gradient (CG) method, ensuring the CG coefficient, β κ, remains integral to the search direction, thereby maintaining the descent property under appropriate line search conditions. Leveraging the strong Wolfe conditions and assuming Lipschitz continuity, we establish the global convergence of the algorithm. Computational experiments demonstrate the algorithm’s superior performance across a range of test problems, including its ability to restore corrupted images with high precision and effectively manage motion control in a 3DOF robotic arm model. These results underscore the algorithm’s potential in addressing key challenges in image processing and robotics.