Iteration method with global convergence 大范围收敛性迭代解法
Iterative method with global convergence 大范围收敛性迭代算法
By means of analysing the global error bound, we prove that the method has a R-linear convergence rate.
借助于全局误差界的分析，证明了所提方法具有R -线性收敛速度。
It is shown that this method possesses global convergence and the penalty parameters are adjusted only finite times under mild conditions.
在较为温和的条件下证明了方法的全局收敛性，及罚参数只需进行有限次调整。
Numerical examples illustrate that the present method possesses both good capability to search global optima and far higher convergence speed than that of chaos optimization method.
算例表明，当混沌搜索的次数达到一定数量时，混合优化方法可以保证算法收敛到全局最优解，且计算效率比混沌优化方法有很大提高。
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