Global convergence of the non - quasi - newton method for unconstrained optimization problems problems 基于非拟牛顿方法无约束最优化问题的全局收敛性
At the second part , nonmonotonic trust region method for unconstrained optimization is studied 第二部分主要研究无约束最优化问题非单调信赖域法。
Algorithms based on trust regions have been shown to be robust for unconstrained optimization problems 摘要信赖域方法是解无约束优化问题的有效的和可靠的方法。
Fourthly , multi - parameter control methods , super - memory gradient methods for solving sub - unconstrained optimization problems are investigated 四是提出多参数控制算法和求解子问题的超记忆梯度算法。
Firstly , we simply introduce the scope , value and developement of the sparse quasi - newton method for unconstrained optimization problems 第一章为绪论简要介绍稀疏拟牛顿法的提出,研究情况及研究价值。
A descent algorithm for solving unconstrained optimization is discussed . global convergence result is established with inexact line search 摘要研究了求解无约束优化问题的一种共轭下降算法,并在非精确线搜索条件下证明了该算法的全局收敛性。
Nocedal and yuan suggested a combination of the trust region and line search method for unconstrained optimization . this mixed technique is called backtraking Nocedal和yuan提出了信赖域与线搜索两种技术相结合的方法,称为回代法( backtracking ) 。
The algorithm is based on a reformulation of the complementarity problem as an unconstrained optimization . it is proved that the algorithm is globally convergent 在将互补问题转化为一个无约束优化问题的基础上,给出了一种求解互补问题的混合方法,证明了该算法的全局收敛性。
Lc1 unconstrained optimization problem was discussed in the second chapter , giving a new trust region method and proving its global convergence and superlinear convergence under some mild conditions 给出了一个新的信赖域算法,并在一定的条件下证明了算法的全局收敛性和局部超线性收敛性。
Many classical testing functions of constrained optimization or unconstrained optimization to be taken as examples , experimental results show that the hybrid algorithm has excellent performance with higher accuracy and speed 以若干经典的无约束和约束优化测试函数作为算例,验证了该混合算法的优越性。