本文我们考虑求解凸约束优化问题的信赖域方法。
In this paper, we develop a trust region algorithm for convex constrained optimization problems.
本文旨在研究求解非凸约束优化问题的基于二阶导数的微分方程方法。
The aim of the dissertation is to study second order derivatives based differential equation approaches to nonconvex constrained optimization problems.
修正算法对于凸约束优化问题全局收敛，最后我们通过一组数值试验验证了新算法的实际运行效果。
Global convergence is promoted through the use of the filter and the convergence theory holds for convex constrained problems. Numerical results demonstrate the efficiency of the modified algorithm.
提出了求解一类带一般凸约束的复合非光滑优化的信赖域算法。
In this paper, we propose a new trust region algorithm for solving a class of composite nonsmooth optimization subject to convex constraints.
系统的稳定界和反馈控制器可以通过求解一类线性矩阵不定式约束的凸优化问题得到。
The robust stable bound and the state feedback controller can be obtained by solving a class of convex optimization problems with LMI constraint.
在部分生成锥内部凸-锥-凸映射下，得到了既有等式约束又有不等式约束的向量优化问题弱有效解的最优性必要条件。
Under the conditions of Partial ic-convex like Maps, optimality necessary conditions of weak efficient solutions for vector optimization problems with equality and inequality constraints are obtained.
在凸规划理论中，通过KT条件，往往将约束最优化问题归结为一个混合互补问题来求解。
In convex programming theory, a constrained optimization problem, by KT conditions, is usually converted into a mixed nonlinear complementarity problem.
该文利用凸优化理论和约束优化理论为前馈神经网络构造出了一个新的优化目标函数。
The paper constructs a new optimal target function for feed forward neural networks according to convex optimization theory and constraint optimization theory.
系统与控制理论中的许多问题，都可转化为线性矩阵不等式约束的凸优化问题，从而简化其求解过程。
Many important problems of system and control theory can be reformulated as linear matrix inequality convex optimization problems, which is numerically tractable.
最后，利用择一性定理，获得了含不等式和等式约束的广义次似凸集值映射向量最优化问题的最优性条件。
Finally, the optimality conditions for vector optimization problems with set valued maps with equality and inequality constraints are obtained with it.
基于混沌神经网络模型可以有效地解决高维、离散、非凸的非线性约束优化问题。
The Chaotic neural network model can be used to solve many multi-dimensioned, discrete, non-convex, nonlinear constrained optimization problems.
通过离线设计一组椭圆不变集，并将其组合成一个终端约束凸集，其中凸集参数作为在线优化变量。
A group of ellipsoidal invariant sets is designed off-line, and then constitutes a terminal constraint convex set whose coefficients are taken as on-line optimization variables.
通过求解一个线性矩阵不等式约束的凸优化问题，提出了最优化保性能控制律的设计方法。
Furthermore, a convex optimization problem with LMI constraints is formulated to design the optimal guaranteed cost controllers.
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.
这两类算法是在ML算法基础上放松约束条件，将问题转化为可在多项式时间内解决的凸优化问题。
These two algorithms relax the constraints of ML algorithm and transform it into a convex problem which can be efficiently solved with a polynomial time.
这两类算法是在ML算法基础上放松约束条件，将问题转化为可在多项式时间内解决的凸优化问题。
These two algorithms relax the constraints of ML algorithm and transform it into a convex problem which can be efficiently solved with a polynomial time.
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