The generalized quasi-newton method for nonconvex unconstrained optimization problems 一般无约束优化问题的广义拟牛顿法
An improvement of simulated annealing algorithm solving unconstrained optimization problem 无约束优化问题模拟退火算法的改进
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
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。
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。
Meanwhile, we point out the privilege and defective of cim . the fourth chapter is the main body of this paper . we explore how to apply the cim to solve unconstrained optimization problems effectively 第四章是本文的主要部分,探讨了锥模型信赖域子问题的求解及不完全锥函数插值模型算法的数值实现。
An algorithm of solving nonlinear coupled equations is given which transforms the solving problem to unconstrained optimization problem and the coupled equations are solved by a genetic algorithm and newton iteration 同时还给出了一种求解非线性方程组的算法,即将非线性方程组的求解问题转化为带约束的优化问题,应用遗传算法和牛顿迭代法求解。
Especially parameter estimation algorithms are discussed and a new method based on bfgs and differential precise linear searching is proposed to solve the unconstrained optimization problem successfully deriving from parameter estimation 研究了机理模型参数估计算法,提出了基于bfgs变尺度和微分精确线性搜索的优化算法求解参数估计优化问题,取得了理想的收敛效果。