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Application of Newton-4EGSOR Iteration for Solving Large Scale Unconstrained Optimization Problems with a Tridiagonal Hessian Matrix

机译:牛顿-4EGSOR迭代法在三对角Hessian矩阵求解大规模无约束优化问题中的应用

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Solving the unconstrained optimization problems using Newton method will lead to the need to solve linear system. Further, the Explicit Group iteration is one of the numerical methods that has an advantage of the efficient block iterative method for solving any linear system. Thus, in this paper to reduce the cost of solving large linear system, we proposed a combination between Newton method with four-point Explicit Group (4-point EG) block iterative method for solving large scale unconstrained optimization problems where the Hessian of the Newton direction is tridiagonal matrices. For the purpose of comparison, we used combination of Newton method with basic iterative method namely successive-over relaxation (SOR) point iteration and Newton method with two-point Explicit Group (2-point EG) block iterative method as reference method. The proposed method shows that the numerical results were more superior compared to the reference methods in term of execution time and number of iteration.
机译:使用牛顿法求解无约束的优化问题将导致需要求解线性系统。另外,显式组迭代是数值方法之一,其具有用于求解任何线性系统的有效块迭代方法的优点。因此,为了减少求解大型线性系统的成本,我们提出了牛顿法与四点显式群(4点EG)块迭代方法的组合,用于解决牛顿Hessian问题的大规模无约束优化问题。方向是三对角矩阵。为了进行比较,我们将牛顿法与基本迭代法(即连续过度松弛(SOR)点迭代)和牛顿法与两点显式群(2点EG)块迭代法的组合作为参考方法。所提出的方法表明,在执行时间和迭代次数方面,数值结果比参考方法更好。

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