| 一类Armijo搜索下新的共轭梯度法及其全局收敛性 |
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| 作者单位： | ;1.北方民族大学数学与信息科学学院;2.河南师范大学数学与信息科学学院;3.河南科技大学数学与统计学院 |
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| 摘 要： | 为有效求解大规模无约束优化问题,提出了一类新的混合共轭梯度法.该方法在每步迭代中都不依赖于函数的凸性和搜索条件而自行产生充分下降方向.在适当的条件下,获证了在Armijo搜索下,即使求解非凸函数极小化的问题,算法也具有全局收敛性.同时,数值实验表明所提算法可以有效求解优化测试问题.
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| 关 键 词： | 共轭梯度法 全局收敛性 充分下降条件 Armijo搜索 |
Global Convergenceof a New Conjugate Gradient Method with Armijo Search |
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| Institution: | ,School of Mathematics and Information,Beifang University of Nationalities,College of Mathematics and Information Science,Henan Normal University,School of Mathematics and Statistics,Henan University of Science and Technology |
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| Abstract: | A new kind of hybrid conjugate gradient method for solving large scale unconstrained optimization problems is proposed.The modified method provides automatically a sufficient descent direction for the objective function at each iteration,aproperty depends neither on the line search used,nor on the convexity of the function.Under appropriate conditions,the proposed method with the Armijo line search converges globally even if the objective function is nonconvex.Numerical results show that the new method is efficient and can be used to deal with some test problems. |
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| Keywords: | global convergence sufficient descent condition Armijo line search |
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