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。
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 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。
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。
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。
Reliability index Robust Convex model Optimization problem;
可靠性指标；稳健；凸模型；优化问题；
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。
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。
On the conditions that vector objective function is like-convex and quasi-convex, we obtain the connectedness of G-proper efficient solution set of the multiobjective optimization problem.
在此基础上，得到了向量目标函数既是似凸又是拟凸的多目标最优化问题的G-恰当有效解集是连通的结论。
On the conditions that vector objective function is like-convex and quasi-convex, we obtain the connectedness of G-proper efficient solution set of the multiobjective optimization problem.
在此基础上，得到了向量目标函数既是似凸又是拟凸的多目标最优化问题的G-恰当有效解集是连通的结论。
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