# Using a New Nonlinear Gradient Method for Solving Large Scale Convex Optimization Problems with an Application on Arabic Medical Text

arXiv: https://arxiv.org/abs/2106.04383 · HTML: https://mlsanity.com/paper/2106.04383/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 27 references that are in the map.
- First posted on arXiv 8 Jun 2021.

## Builds on (most cited first, 5 of 5)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Trust-Region Algorithms for Training Responses: Machine Learning Methods Using Indefinite Hessian Approximations](https://mlsanity.com/paper/1807.00251/) (2018)
- [A Robust Multi-Batch L-BFGS Method for Machine Learning](https://mlsanity.com/paper/1707.08552/) (2017)

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

- [New Arabic Medical Dataset for Diseases Classification](https://mlsanity.com/paper/2106.15236/) (2021)

## Abstract (arXiv)

Gradient methods have applications in multiple fields, including signal processing, image processing, and dynamic systems. In this paper, we present a nonlinear gradient method for solving convex supra-quadratic functions by developing the search direction, that done by hybridizing between the two conjugate coefficients HRM [2] and NHS [1]. The numerical results proved the effectiveness of the presented method by applying it to solve standard problems and reaching the exact solution if the objective function is quadratic convex. Also presented in this article, an application to the problem of named entities in the Arabic medical language, as it proved the stability of the proposed method and its efficiency in terms of execution time.