unconstrained optimization problem造句

• 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。
• Newton-pcg algorithm is very efficient for solving the unconstrained optimization problems . we considered an integer one-dimensional optimization problem appeared in newton-pcg method . the efficiency of newton-pcg algorithm is depended on the optimal value of this problem
  newton-pcg算法是解决无约束问题的有效方法，在该算法中需要求解一个一维整数最优化问题，并且算法的效率也依赖于它的最优值。
• In this paper we reformulate gcp a sasystem of nonlinear equations, and the gcp is reformulated as unconstrained optimization problem, as for the optimization problem, the damped gauss-newton method algorithm of two kinds of steps is employed for obtaining its solution, and the global convergence analysis are given in this thesis
  摘要本文将广义互补问题转化为一个非线性方程组问题，然后建立了gcp问题的无约束优化问题的转化形式，对该优化问题，用两种步长下的阻尼高斯牛顿算法来求解，并给出了两种情况下算法的全局收敛性。
• The parameter control methods are very similar to penalty function methods, both of them are to solve constrained optimization problems by solving a series of sub-unconstrained optimization problems . but parameter control methods are different from penalty function methods . firstly, the penalty coefficient of penalty function methods are preassigned, while the parameters of parameter control methodsare generated automatically according to some rule prescribed
  参数控制算法虽然与罚函数法非常类似，都是通过求解一系列无约束极小化问题来逼近约束优化问题的最优解，但罚函数法中的罚因子是预先设定的，而参数控制算法中的参数是自动产生的。
• In this thesis, we mainly discuss the algorithm and the theory of conjugate gradient method . the structure of this paper is organized as follows : in the first chapter we survey the history of conjugate gradient method, and discuss five conjugate gradient methods respectively which are fr method, prp method, hs method, cd method, dy method . they are currently considered to be well-known methods for large scale unconstrained optimization problems
  本文主要研究了共轭梯度法的算法和理论，得到了一些理论和数值结果。文中首先介绍共轭梯度法的发展概况，对几种著名的共轭梯度法fr方法，prp方法，hs方法，cd方法和dy方法的全局收敛性，全局有效性及数值计算作了综述。