Trust region methods are efficient for solving unconstraint optimization problems.
信赖域算法是求解最优化问题的一类有效算法。
An attractive property of trust region methods lies in their numerical stability and robustness.
信赖域算法的一个显著优点是其稳定的数值性能。
In this paper, we propose a new class of trust region methods for nonlinear optimization problems.
本文提出一类新的解无约束最优化问题的信赖域方法。
In this paper, we mainly discuss the modified quasi-Newton trust region methods based on a conic model (TRCM method) and prove their convergence properties.
沿袭用锥模型来逼近原问题的思路，本文主要研究锥模型拟牛顿信赖域方法的参数选择、收敛性和数值实现。
Both line search and trust region algorithm are well-accepted methods in the optimization to assure global convergence.
线性搜索方法和信赖域方法是保证最优化问题的整体收敛性的两种基本策略。
Trust region method is a kind of efficient methods to solve the general unconstrained optimization and its special situation, the nonlinear least squares problems.
对于一般的无约束最优化问题及其特殊情况非线性最小二乘问题而言，信赖域方法是一种有效的方法。
A trust-region methods which were replaced by line search methods were adopted to assure the global .
为了保证算法的总体收敛性，应用信赖域算法代替一维搜索，确定下一个迭代点。
The basic idea of these methods is to approximate the optimization problem by a sequence of quadratic minimization problems subject to some trust region.
该类算法的基本思想是通过求解一系列二次函数在信赖域中的极小值点逼近最优化问题的解。
The basic idea of these methods is to approximate the optimization problem by a sequence of quadratic minimization problems subject to some trust region.
该类算法的基本思想是通过求解一系列二次函数在信赖域中的极小值点逼近最优化问题的解。
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