The problem is reduced to a linear convex optimization algorithm via LMI approach.
采用线性矩阵不等式方法，将问题转化为一个线性凸优化算法。
In this thesis, we study two related problems: convex optimization problems and equilibrium problems.
凸优化问题与平衡问题密切相关，本文对这二类问题进行研究。
This paper presents an interior trust region method for linear constrained LC convex optimization problems.
本文提出一种解线性约束凸规划的数值方法。
Furthermore, a convex optimization problem with LMI constraints is formulated to design the optimal guaranteed cost controllers.
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。
The convex optimization algorithm was used to get the minima upper bound of performance cost and parameter of optimal minimax controller.
引入凸优化算法，求解使闭环系统渐近稳定且性能指标上界最小的最优控制器参数。
Based on the convex optimization, the algorithm jointly optimizes power allocation, subcarrier pairing, relay selection and user selection.
该算法基于凸优化理论，对功率分配、子载波配对、中继选择和用户选择进行了联合优化。
Converting the convex optimization question to simple one dimension search, we get the effective method to settle the optimal weight coefficient.
通过转换该凸最优的约束方程为简单的一维搜索，提出了最优加权系数的有效求解方法。
The robust stable bound and the state feedback controller can be obtained by solving a class of convex optimization problems with LMI constraint.
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。
On the basis of convex optimization theory, the design arithmetic method and its steps about optimal guaranteed cost reliable control are provided.
根据凸优化理论，最优保性能标准控制器和最优保性能可靠控制器的设计方法转化为一个线性凸优化算法。
This paper discusses problems arising in system and control theory to a few standard convex optimization problems involving linear matrix inequality (LMI).
本文研究了出现在系统与控制理论中的一些标准的、包含线性矩阵不等式的凸优化问题。
The paper constructs a new optimal target function for feed forward neural networks according to convex optimization theory and constraint optimization theory.
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。
Many important problems of system and control theory can be reformulated as linear matrix inequality convex optimization problems, which is numerically tractable.
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。
On the basis of convex optimization theory, the design method about optimal guaranteed cost reliable controller and standard controller transform convex optimization arithmetic.
根据凸优化理论，最优保性能标准控制器和最优保性能可靠控制器的设计方法转化为一个线性凸优化算法。
Using the linear matrix inequality (LMI) technique, the problem is converted into a linear convex optimization algorithm so that a global optimization solution is obtained. Finally.
采用线性矩阵不等式技术，将问题转化为一线性凸优化算法，可得问题的全局最优解。
Then, by use of multiple carrier system's frequency-sharing property and convex optimization, a subcarrier and power optimal allocation algorithm is proposed based on Lagrangian duality theory.
然后，运用凸优化技术分析了该资源分配问题，并基于拉格朗日对偶法给出了一种子载波和功率最优分配算法。
In this paper, we propose a new trust region algorithm for solving a class of composite nonsmooth optimization subject to convex constraints.
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 convex programming theory, a constrained optimization problem, by KT conditions, is usually converted into a mixed nonlinear complementarity problem.
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。
The optimization problem can be solved based on the density-stiffness interpolation scheme and the method of moving asymptotes belonging to sequential convex programming approaches.
采用基于密度刚度插值模型和序列凸规划法中的移动渐近线方法求解优化模型。 通过经典算例验证了本方法的有效性。
This dissertation studies systematically fuzzy convex analysis and fuzzy optimization and the relationships between them.
本文系统地研究了模糊凸分析与模糊优化及其它们之间的联系。
Many scientific, engineering and economic areas involve the optimization of complex, nonlinear and possibly non-convex problems.
科学领域，工程领域和经济领域都涉及到很多复杂的、非线性的甚至非凸形式的最优化问题。
In this paper, we develop a trust region algorithm for convex constrained optimization problems.
本文我们考虑求解凸约束优化问题的信赖域方法。
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。
SVM transforms machine learning to solve an optimization problem, and to solve a convex quadratic programming problem by the optimization theory and method constructing algorithms.
它将机器学习问题转化为求解最优化问题，并应用最优化理论构造算法来解决凸二次规划问题。
Due to the non-convex of the prior function and hyper-parameters, we use the dynamic posterior simulation rather than the general optimization methods to get reconstruction image.
由于采用的先验函数是非凸的并包含超验参数，一般的优化方法难以处理，采用动态后验模拟的方法可以很好地解决这些问题。
The problem of resource allocation in this scheme is a non-convex non-linear optimization problem.
该模型中的资源分配问题是一个非凸的非线性优化问题。
The Chaotic neural network model can be used to solve many multi-dimensioned, discrete, non-convex, nonlinear constrained optimization problems.
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。
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。
应用推荐