A challenge to huge scale semidefinite programs-exploiting sparsity, parallel computation and polynomial optimization problems

我们研究了求解半定规划的原对偶内点法，这是最重要的优化问题之一，在科学和工程的各个领域都有广泛的应用，并在此基础上开发了软件包SDPA。 SDPA 比现有软件包在更短的时间内解决了更大规模的问题。作为 SDPA 的应用，我们提供了用于多项式优化问题的 SparsePOP 和用于大规模传感器网络定位问题的 SFSDP。

We studied primal-dual interior-point methods for solving a semidefinite program which is one of the most important optimization problems having lots of applications in various fields of science and engineering, and developed a software package SDPA based on them. SDPA solves larger scale problems in shorter time than the existing software packages. As applications of SDPA, we provided SparsePOP for polynomial optimization problems and SFSDP for large scale sensor network localization problems.