得出了无约束问题、约束问题的相关条件。
Some relevant conditions on unconstrained and constrained problems are obtained.
借助不可微精确罚函数把约束问题转化为单个无约束问题来处理。
Nondifferentiable exact penalty functions are used to transform a constrained optimization problem into a single unconstrained optimization problem.
并且建立了原问题的K-T点与等价无约束问题的稳定点之间的关系。
Moreover, we get the relationship between the K-T point of the primal problem and the stationary point of the unconstrained problem.
对于控制变量受箱型约束的问题，采用三角函数转换将其转化为控制无约束问题。
For boxing constraint of control, trigonometric function transformation was developed to achieve an unconstrained problem.
第六章，由第五章求解无约束问题的多维滤子信赖域修正算法出发，将其推广到界约束乃至凸约束的优化问题。
Numerical results show that the present algorithm is efficient and reliable. The last chapter, we present a modified filter trust region method for bound constrained problems.
这里的根本问题是，人人都忘记了一个无约束系统具有弹性体模态以及刚体模态。
The basic problem here is that everyone forgot that an unconstrained system has flexible modes AND the rigid body modes.
无约束优化问题的共轭梯度路径构造的思想启迪我们用其来解带线性等式约束和有界变量约束的优化问题。
The idea of conjugate gradient path in unconstrained optimization irradiates us to use this method for solving the linear equality optimization subject to bounds on variables.
本文提出一类求解无约束优化问题的非单调曲线搜索方法，在较弱条件下证明了其收敛性。
This paper presents a non-monotone curve search method for unconstrained optimization problems and proves its convergence under some mild conditions.
针对无约束函数最优化问题，提出了一种能有效加快收敛速度的改进单纯形算法。
An improved simplex method (ISM) based on Nelder and Meads simplex method (N-M SM) is presented for unconstrained function optimization.
BFGS算法是解无约束优化问题的公认的最有效的算法之一。
BFGS algorithm is one of the most effective methods in solving the non-constrained optimization problems.
本文阐明无约束和有约束非线性问题的基本解法，并说明非线性规划的线性近似方法。
This article deals with the basic methods for solving unconstrained and constrained nonlinear problems. It describes also the linear approximation methods of nonlinear programming.
通过变换可将该无约束优化问题转化为求解非线性代数方程组的问题。
This unconstrained optimization problem may be transformed into nonlinear algebraic system of equations.
用序列无约束极小化方法（SUMT）求解非线性有约束的优化问题。
The non linear optimization problem with constraint has been solved by use of the sequential unconstrained minimization technique (SUMT).
共轭梯度法是求解无约束优化问题的一类有效方法。
The conjugate gradient method is one of the most efficient methods for solving unconstrained optimization problems.
提出了一种新的蚁群算法来求解无约束的整数规划问题。
A new ACO algorithm for unconstrained nonlinear integer optimization problem is present.
共轭梯度法是求解大规模无约束优化问题的一种有效方法。
Conjugate gradient method is an efficient method in solving problems with unconstrained optimization, which is especially efficient in dealing large dimension.
由于优化问题可能是不连续的、不可微的甚或是没有函数解析式的，传统经典的无约束优化方法在应用时会受到限制。
Because the optimization problem may be discontinuous and non-differentiable even has no objective function, the traditional optimization methods are unable to tackle with it.
PR共轭梯度法是求解大型无约束优化问题的有效算法之一，但是算法的全局收敛性在理论上一直没有得到解决。
Pr conjugate gradient method is one of the efficient methods for solving large scale unconstrained optimization problems, however, its global convergence has not been solved for a long time.
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 propose a new class of trust region methods for nonlinear optimization problems.
填充函数法是一种解无约束全局极小化问题的方法。
The filled function method is an approach for solving unconstrained global minimization problem.
罚函数法将约束条件加到目标函数上，变有约束为无约束，能较易求解该类问题。
But penalty function method can solve this problem easily by adding restrictions to target function, which becomes restrictions to nonrestrictions.
图像重建常常被转化为解非线性无约束极值问题。
The image reconstruction is often converted to be the nonlinear unconfined extreme problem.
无约束的航路规划问题，实际上是一个TSP问题，目前还没有求解TSP问题的比较有效的实时算法。
The question of flight path programming is TSP problem, regardless of other constraints. There are no effective real time algorithms of TSP problem at present.
研究给出了一类新的求解无约束优化问题的下降算法。
In this paper, a class of new descent algorithm is proposed to solve unconstrained optimization problems.
对于一般的无约束优化问题，信赖域方法是一种比较有效的方法。
Trust region method is a kind of efficient and robust method to solve general unconstrained optimization.
这里考察了求解无约束总体极小化问题的神经网络方法，提出了一种新的网络求解模型。
Neural network method for solving global unconstrained minimized problems was investigated and a new neural network model was then proposed.
这里考察了求解无约束总体极小化问题的神经网络方法，提出了一种新的网络求解模型。
Neural network method for solving global unconstrained minimized problems was investigated and a new neural network model was then proposed.
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