unconstrained optimization

The convergence of a random search algorithm for unconstrained optimization problems

无约束最优化问题随机搜索算法的收敛性

The conjugate gradient method is one of the most efficient methods for solving unconstrained optimization problems.

共轭梯度方法是求解无约束优化问题的一类有效方法.

A new gradient descent algorithm for unconstrained optimization problem is proposed.

提出了一种新的无约束优化下降算法.

We discuss non-monotonic trust region algorithm for unconstrained nonlinear optimization problems in view of Trust Region Model mainly.

然后从信赖域子问题的角度出发，对无约束优化问题提出了一个改进的非单调信赖域算法。

In referrence [ 4], the Polak? Ribi é re? Polyak ( PRP) conjugate gradient method for unconstrained optimization is considered and the conclusion is obtained that the PRP method is global convergent for uniformly convex functions under the Wolfe line search.

已有文献对求解无约束优化问题的Polak Ribi啨re Polyak（PRP）共轭梯度法进行了研究并得出结论：采用Wolfe线性搜索确定步长的PRP方法对一致凸函数具有全局收敛性。

Chapter 3 proposes a proximal bundle method with inexact data for nonsmooth convex unconstrained optimization.

在第二章的基础上，提出了一种解决非光滑无约束优化问题的迫近束方法。

Total gradient method can also be used for solving unconstrained optimization problems.

共梯度法也可以用于求解无约束优化问题.

Convergence analysis of methods based on BDF for unconstrained optimization

基于BDF的无约束优化方法的收敛性分析

BFGS algorithm using large-scale unconstrained optimization problems, the code language for Fortran 77.

利用BFGS算法求解大规模无约束最优化问题, 代码所用语言为Fortran77.

The method of multiplies is used to transform constrained optimization into unconstrained optimization.

文中采用乘子法将约束优化问题化为无约束优化问题。

Global convergence for the algorithm is established under the assumption that the objective function is bounded from below, The authors also generalize the algorithm to constrained convex optimization and obtain the convergence that is the same as unconstrained algorithm.

此外还把算法推广到约束凸规划问题，得到了与无约束问题同样的收敛结果，如果约束是线性的，则算法是数值可行的。

Aggregate function which approximates the maximum function, is introduced, and data clustering problem is reformulated as the unconstrained optimization.

借助于近似极大值函数的凝聚函数，将传统数据聚类问题转化为无约束优化问题求解。

Unconstrained optimization methods include gradient, conjugate direction, Newton, and quasi-Newton methods.

无约束最优化方法包括梯度法 、 共轭方向法 、 牛顿法和拟牛顿法.

Analysis of Methods for Unconstrained Optimization Based on Dynamical Systems

基于动力系统的无约束优化问题的方法分析