Pyomo—Optimization Modeling in Python William e.Hart· Carl laird·Jean- Paul watson

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

斯坦福的凸优化课件，及这节配套书籍、习题集。都是英文非扫描版。

凸优化 Convex Optimization by Stephen Boyd 外加课本习题解析

This book details the significant performance tradeoffs available in analog CMOS design and guides the designer towards optimum design by describing: An interpretation of MOS modeling for the analog designer, motivated by the EKV MOS model, using tabulated hand expressions and figures that give performance and tradeoffs for the design choices of drain current, inversion coefficient, and channel length; performance includes effective gate-source bias and drain-source saturation voltages, transconductance efficiency, transconductance distortion, normalized drain-source conductance, capacitances, gain and bandwidth measures, thermal and flicker noise, mismatch, and gate and drain leakage current Measured data that validates the inclusion of important small-geometry effects like velocity saturation, vertical-field mobility reduction, drain-induced barrier lowering, and inversion-level increases in gate-referred, flicker noise voltage In-depth treatment of moderate inversion, which offers low bias compliance voltages, high transconductance efficiency, and good immunity to velocity saturation effects for circuits designed in modern, low-voltage processes Fabricated design examples that include operational transconductance amplifiers optimized for various tradeoffs in DC and AC performance, and micropower, low-noise preamplifiers optimized for minimum thermal and flicker noise A design spreadsheet, available at the book web site, that facilitates rapid, optimum design of MOS devices and circuits

convex optimization solution

Convex optimization 凸优化 stephen boyd

灰鲸优化算法优化退火算法，可以用于对各类数据的特征选择

CPLEX英文学习资料。 IBM.ILOG.CPLEX.Optimization.Studio.Getting.Start.with.CPLEX.v12.4

Multitask-Learning 多任务学习相关资料，主要包括代表性学者主页、论文、综述、幻灯片、论文集和开源代码。欢迎分享~ This repository collects Multitask-Learning related materials, mainly including the homepage of representative scholars, papers, surveys, slides, proceedings, and open-source projects. Welcome to share these materials! Something New!!! Homepage ML^2 @ UCF Elisa Ricci Gjorgji Strezoski Machine Learning with Interdependent and Non-ide