Derivative Free Conjugate Gradient Method via Broyden's Update for solving symmetric systems of nonlinear equations
The applications of mathematics in many areas of computing, scientific and engineering research mostly give rise to a systems of nonlinear equations. Various iterative methods have been developed to solve such equations, this includes Newton method, Quasi-Newton's etc. Over the years, there h...
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| 主要な著者: | , , , , |
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| フォーマット: | Conference or Workshop Item |
| 言語: | English |
| 出版事項: |
2019
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| 主題: | |
| オンライン･アクセス: | http://eprints.unisza.edu.my/1923/1/FH03-FIK-19-36120.pdf http://eprints.unisza.edu.my/1923/ |
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| 要約: | The applications of mathematics in many areas of computing, scientific and engineering research mostly give rise to a
systems of nonlinear equations. Various iterative methods have been developed to solve such equations, this includes
Newton method, Quasi-Newton's etc. Over the years, there has been significant theoretical study on quasi-Newton
methods for solving such systems, but unfortunately the methods suffers setback. To overcome such problems, a
Derivative free Method for Solving Symmetric Systems of Nonlinear Equations Using Broyden's Update is presented.
The modification is achieved by simply approximating the inverse Hessian matrix to with (δ and I represents
acceleration parameter and an identity matrix respectively) without computing any derivative. The method uses the
symmetric structure of the system sufficiently and the generalized classical Broyden's update method for
unconstrained optimization problems. The squared norm merit function is used, both the direction and the line
search technique are derivative-free, this attractive feature of the proposed method makes it to have a very low storage
requirement thereby solving large scale problems successfully. In an effort to solve nonlinear problems of the form F(x)
= 0, 0, x ∈ R different initial starting points were used on a set of benchmark test problems, the output is based on
number of iterations and CPU time. A comparison between the proposed method and the classical methods were
made and found that the proposed method is efficient, robust and outperformed the existing method. |
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