New approach for Secant Update generalized version of PSB

Nicolas Boutet, R. Haelterman, J. Degroote
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Abstract

Working with Quasi-Newton methods in optimization leads to one important challenge, being to find an estimate of the Hessian matrix as close as possible to the real matrix. While multisecant methods are regularly used to solve root finding problems, they have been little explored in optimization because the symmetry property of the Hessian matrix estimation is generally not compatible with the multisecant property. In this paper, we propose a solution to apply multisecant methods to optimization problems. Starting from the Powell-Symmetric-Broyden (PSB) update formula and adding pieces of information from the previous steps of the optimization path, we want to develop a new update formula for the estimate of the Hessian. A multisecant version of PSB is, however, generally mathematically impossible to build. For that reason, we provide a formula that satisfies the symmetry and is as close as possible to satisfy the multisecant condition and vice versa for a second formula. Subsequently, we add enforcement of the last secant equation to the symmetric formula and present a comparison between the different methods.
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割线更新PSB广义版的新方法
使用准牛顿方法进行优化会导致一个重要的挑战,即找到尽可能接近实际矩阵的Hessian矩阵的估计。虽然多次割线方法通常用于求解寻根问题,但由于Hessian矩阵估计的对称性通常与多次割线性质不兼容,因此在优化方面的探索很少。在本文中,我们提出了一种将多重割线方法应用于优化问题的解决方案。从Powell-Symmetric-Broyden (PSB)更新公式出发,加入优化路径前面步骤的信息,我们想要开发一个新的更新公式来估计Hessian。然而,PSB的多割线版本通常在数学上是不可能构建的。出于这个原因,我们提供了一个公式,它满足对称性,并尽可能地满足多重割线条件,反之亦然,对于第二个公式。随后,我们在对称公式中加入了最后一个正割方程的执行,并对不同方法进行了比较。
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