这是HPIPM，一种高性能的内点方法求解器，用于密集的，最优的控制结构和树形结构的凸二次方程序。 它提供有效的密集算法和结构探索算法的实现，以解决一般在模型预测控制和嵌入式优化中出现的中小型问题，并且它依赖于高性能线性代数程序包BLASFEO。 HPIPM（和BLASFEO，这是一个依赖项），同时包含make和cmake构建系统。 首选的是make ，它可以用来编译和运行任何语言的任何库，接口和示例。 make也用于连续集成travis脚本中。 cmake只能用于编译库，而感兴趣的用户应通过从各种Makefile的命令中Makefile灵感来编译接口并自己运行示例。 入门： 开始使用HPIPM的最佳方法是查看/hpipm/examples/ 。 HPIPM可以从C直接使用，但是也有到Python和Matlab的接口。 根据您要使用HP​​IPM的级别，请查看下面的以下部分。 可以在do

一个Mathlab算法，主要用来解决karmarkar 内点法

内部积分法 在MATLAB中实现内部点方法（我在线性优化课程[MTH305] [IIIT-Delhi]中的分配）。 此外，我们解决了以下线性问题： min 1'u + 1'v st. a'xi + b >= 1 - ui i=1..N a'yj + b <= -1 + vj j=1..M u >= 0 v >= 0 我们得到了： DB_Vecs.npy （用于训练的序列向量） DB_Labels.npy （对应的标签） Q_Vecs.npy （测试序列） 将它们转换为Matrix文件 。 我们Q_vecs list / numpy数组的形式提交结果（ Q_vecs标签）。 在这里，输出在Matrix文件。 参考 影片资源： 斯蒂芬·博伊德（Stephen Boyd），第18届会议，凸面优化（讨论

讲线性优化内点法算法的经典参考书，英文版的，读下来绝对有收获

内点法讲义 (ORIE 6300 Lecture Notes)

In the past decade, primal-dual algorithms have emerged as the most important and useful algorithms from the interior-point class. This book presents the major primal-dual algorithms for linear programming in straightforward terms. A thorough description of the theoretical properties of these methods is given, as are a discussion of practical and computational aspects and a summary of current software. This is an excellent, timely, and well-written work. The major primal-dual algorithms covered in this book are path-following algorithms (short- and long-step, predictor-corrector), potential-reduction algorithms, and infeasible-interior-point algorithms. A unified treatment of superlinear convergence, finite termination, and detection of infeasible problems is presented. Issue relevant to practical implementation are also discussed, including sparse linear algebra and a complete specification of Mehrota's predictor-correction algorithm. Also treated are extensions of primal-dual algorithms to more general problems such as monotone complementarity, semidefinite programming, and general convex programming problems. Some background in linear programming and its associated duality theory, linear algebra, and numerical analysis is helpful, although an extensive appendix ensures that the book is largely self-contained. The book is useful for graduate students and researchers in the sciences and engineering who are interested in using large-scale optimization techniques in their research, including those interested in original research in interior-point methods. Engineers may also find applications to problems of process control, predictive control, or design optimization. The book may also be used as a text for a special topics course in optimization or a unit of a general course in optimization or linear programming. Researchers and students in the field of interior-point methods will find the book invaluable as a reference to the key results, the basic analysis in the

凸优化工具

包含了一个内点约束的最优控制问题MATLAB数值求解，示例程序，非常简单，适用于初学者。需要已经安装GPOPS-II软件包。