The strong convergence of the algorithm under a general choice for radius of trust region is proved.

在一个很广泛的信赖域半径选择规则下，证明了算法的强收敛性。

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Numerical results show that we choose a suitable initial adaptive trust region radius at each iteration so as to reduce the number of iterations, function and gradient evaluations.

数值试验结果表明，我们改进后的信赖域半径使算法在迭代过程中十分有效，不但减少了迭代次数还减少了函数和梯度的赋值。

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Numerical results show that we choose a suitable initial adaptive trust region radius at each iteration so as to reduce the number of iterations, function and gradient evaluations.

数值试验结果表明，我们改进后的信赖域半径使算法在迭代过程中十分有效，不但减少了迭代次数还减少了函数和梯度的赋值。

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