A quasi - newton method on convex optimization 求解凸规划问题的一种拟牛顿算法
For some special cases , the deterministic convex optimization problems are derived 对某些特殊的情形,我们导出了鲁棒线性最优化的确定性等价问题。
This paper presents an interior trust region method for linear constrained lc ^ 1 convex optimization problems 摘要本文提出一种解线性约束凸规划的数值方法。
Then by building and solving the convex optimization problem , the optimal upper bound of performance index and parameters of the state feedback controller is given 通过建立、求解凸优化问题得到最优极小极大控制器参数和性能指标的最小上界。
Abstract : this paper considers the decentralized stabilization problem via local state feedback control laws for a class of large - scale linear discrete - time systems with delay interconnections . a sufficient condition for decentralized stabilizability is derived and is expressed as a system of linear matrix inequalities . furthermore , the problem of designing a decentralized state feedback control law with smaller feedback gain parameters is formulated as a convex optimization problem , and latter can be solved by using existing efficient convex optimization techniques . the obtained controller enables the closed - loop systems to be not only stable , but also of any prescribed stability degree 文摘:用一组线性矩阵不等式给出一类线性离散时滞大系统分散能镇定的一个充分条件,进而,通过建立和求解一个凸优化问题,提出了具有较小反馈增益参数的分散稳定化状态反馈控制律的设计方法.所得到的控制器不仅使得闭环系统是稳定的,而且还可以使得闭环系统状态具有给定的衰减度
Convex minimization, a subfield of optimization, studies the problem of minimizing convex functions over convex sets. The convexity property can make optimization in some sense "easier" than the general case - for example, any local minimum must be a global minimum.