A New Paired Spectral Gradient Method to Improve Unconstrained and Non-Linear Optimization

Siham I. Aziz, Zeyad M. Abdullah
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引用次数: 0

Abstract

The conjugated spectral gradient (SCG) method is an effective method for non-constrained large-scale nonlinear optimization. In this work, a new spectral conjugate gradient method is proposed with a strong Wolfe-Powell line search (SWP). The new proposal is based on using the formula obtained by comparing the proposed algorithm with previously published conjugate gradient algorithms. Under the usual assumptions, the descent properties and overall global convergence of the proposed method are proved. The proposed method is numerically proven to be effective.
改进无约束非线性优化的一种新的成对谱梯度方法
共轭谱梯度(SCG)方法是一种有效的无约束大规模非线性优化方法。在这项工作中,提出了一种新的具有强Wolfe-Powell线搜索(SWP)的谱共轭梯度方法。新的建议是基于使用通过将所提出的算法与先前发表的共轭梯度算法进行比较而获得的公式。在通常的假设下,证明了该方法的下降性质和全局收敛性。数值证明了该方法的有效性。
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3
审稿时长
10 weeks
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