CVXMOD is a Python-based tool for expressing and solving convex optimization problems.
标签： Python-based optimization expressing problems
上传时间： 2013-12-13
上传用户：changeboy
convex optimization problem
标签： convex optimization
上传时间： 2015-03-23
上传用户：飞来大货车
一篇同学的硕士论文 --OFDMA Ressource Allocation using Multiuser Constant-Power Waterfilling. 关于OFDMA 资源分配的算法 用到凸优化(convex optimization)的理论 证明对多用户的Waterfilling算法可以实现优化原则
标签： OFDMA Constant-Power Waterfilling Allocation
上传时间： 2014-08-22
上传用户：GHF
Description The art galleries of the new and very futuristic building of the Center for Balkan Cooperation have the form of polygons (not necessarily convex). When a big exhibition is organized, watching over all of the pictures is a big security concern. Your task is that for a given gallery to write a program which finds the surface of the area of the floor, from which each point on the walls of the gallery is visible. On the figure 1. a map of a gallery is given in some co-ordinate system. The area wanted is shaded on the figure 2.
标签： Description futuristic galleries the
上传时间： 2017-02-17
上传用户：1427796291
We consider the problem of target localization by a network of passive sensors. When an unknown target emits an acoustic or a radio signal, its position can be localized with multiple sensors using the time difference of arrival (TDOA) information. In this paper, we consider the maximum likelihood formulation of this target localization problem and provide efficient convex relaxations for this nonconvex optimization problem.We also propose a formulation for robust target localization in the presence of sensor location errors. Two Cramer-Rao bounds are derived corresponding to situations with and without sensor node location errors. Simulation results confirm the efficiency and superior performance of the convex relaxation approach as compared to the existing least squares based approach when large sensor node location errors are present.
标签： 传感器网络
上传时间： 2016-11-27
上传用户：xxmluo
The basic topic of this book is solving problems from system and control theory using convex optimization. We show that a wide variety of problems arising in system and control theory can be reduced to a handful of standard convex and quasiconvex optimization problems that involve matrix inequalities. For a few special cases there are “analytic solutions” to these problems, but our main point is that they can be solved numerically in all cases. These standard problems can be solved in polynomial- time (by, e.g., the ellipsoid algorithm of Shor, Nemirovskii, and Yudin), and so are tractable, at least in a theoretical sense. Recently developed interior-point methods for these standard problems have been found to be extremely efficient in practice. Therefore, we consider the original problems from system and control theory as solved.
标签： Linear_Matrix_Inequalities_in_Sys tem
上传时间： 2020-06-10
上传用户：shancjb
