On exact line search method for a polynomial matrix equation

Chacha Stephen Chacha
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Abstract

In this work, we investigate the elementwise minimal non-negative (EMN) solution of the matrix polynomial equation using an exact line search (ELS) technique to enhance the convergence of the Newton method. Nonnegative solutions to matrix equations are essential in engineering, optimization, signal processing, and data mining, driving advancements and improving efficiency in these fields. While recent advancements in solving matrix equations with nonnegative constraints have emphasized iterative methods, optimization strategies, and theoretical developments, efficiently finding the EMN solution remains a significant challenge. The proposed method integrates the Newton method with an exact line search (ELS) strategy to accelerate convergence and improve solution accuracy. Numerical experiments demonstrate that this approach requires fewer iterations to reach the EMN solution compared to the standard Newton method. Moreover, the method shows improved stability, particularly when dealing with ill-conditioned input matrices and very small tolerance errors.
多项式矩阵方程的精确直线搜索方法
在这项工作中,我们研究了矩阵多项式方程的元素最小非负(EMN)解,使用精确线搜索(ELS)技术来提高牛顿方法的收敛性。矩阵方程的非负解在工程、优化、信号处理和数据挖掘中是必不可少的,它推动了这些领域的进步和提高了效率。虽然最近求解非负约束矩阵方程的进展强调了迭代方法、优化策略和理论发展,但有效地找到EMN解仍然是一个重大挑战。该方法将牛顿法与精确线搜索(ELS)策略相结合,加快了收敛速度,提高了求解精度。数值实验表明,与标准牛顿法相比,该方法求解EMN所需的迭代次数较少。此外,该方法还显示出更好的稳定性,特别是在处理病态输入矩阵和非常小的公差误差时。
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