粒子群优化方法 (PSO)，这是一种元启发式算法，模仿鱼类和鸟类等社会行为动物寻找食物。 代码是不言自明的。 有一篇论文提供了足够的背景信息来理解此代码。 《粒子群优化与差分进化算法：技术分析， 应用和杂交观点”，作者：Swagatam Das、Ajith Abraham 和 Amit Konar

osqp-matlab：OSQP的Matlab接口

翼型的空气动力学优化 使用进化算法对机翼进行空气动力学优化。 动机 该项目于2016年5月完成，目的是对ISAE-SUPAERO研究生院第二年的粘性空气动力学课程进行最终评估。 方法 目的是找到一种在滑流条件下能最大化给定性能标准的滑翔机翼型。 选择类形状变换（CST）可以对机翼几何形状进行数学建模，因为它所需的参数数量少且具有强大的建模能力。 CST还可以轻松确保前后缘的几何形状一致。 使用了两种不同的优化算法： 首先实现了遗传算法，其中CST参数充当“染色体”，而机翼充当“个体”。 然后实施了混合遗传算法，包括两个步骤。 第一步与遗传算法相同，其中第二步执行约束优化，以进一步利用先前发现的局部吸引区。 迄今为止，仅遗传算法已上传。 先决条件 该项目是用MATLAB编写的，因此需要MATLAB的副本。 它还使用了MATLAB的Global Optimization Toolbox的

Intel® Quartus® Prime Standard Edition Handbook Volume 2 Design Implementation and Optimization.pdf

这是非常经典的最优化教材，主要涉及非线性优化

distributionally_robust_optimization 论文中实现的方法： 约束随机系统的分布鲁棒控制 使用Wasserstein指标的数据驱动的分布式鲁棒优化：性能保证和易于重构

Bertsimas D,BrownDB,CaramanisC.Theory and applications of robust optimization. SIAMReview2011;53(3):464–501.

深度学习理论是当下研究的热点之一。最近来自UIUC计算机助理教授Sun Ruoyu撰写一篇深度学习最优化理论和算法的综述论文，共60页257篇文献，概述了神经网络的优化算法和训练理论《Optimization for deep learning: theory and algorithms》，并得到众多大佬的推荐，比如模仿学习带头人加州理工Yisong Yue，欢迎大家阅览，需要一番数学理论功底，方能扛过。

IBM ILOG CPLEX Optimization Studio Free Edition V12.9 for Linux x86-64

K. Miettinen, Nonlinear Multiobjective Optimization. Norwell, A:Kluwer, 1999. Problems with multiple objectives and criteria are generally known as multiple criteria optimization or multiple criteria decision-making (MCDM) problems. So far, these types of problems have typically been modelled and solved by means of linear programming. However, many real-life phenomena are of a nonlinear nature, which is why we need tools for nonlinear programming capable of handling several conflicting or incommensurable objectives. In this case, methods of traditional single objective optimization and linear programming are not enough; we need new ways of thinking, new concepts, and new methods - nonlinear multiobjective optimization. Nonlinear Multiobjective Optimization provides an extensive, up-to-date, self-contained and consistent survey, review of the literature and of the state of the art on nonlinear (deterministic) multiobjective optimization, its methods, its theory and its background. The amount of literature on multiobjective optimization is immense. The treatment in this book is based on approximately 1500 publications in English printed mainly after the year 1980. Problems related to real-life applications often contain irregularities and nonsmoothnesses. The treatment of nondifferentiable multiobjective optimization in the literature is rather rare. For this reason, this book contains material about the possibilities, background, theory and methods of nondifferentiable multiobjective optimization as well. This book is intended for both researchers and students in the areas of (applied) mathematics, engineering, economics, operations research and management science; it is meant for both professionals and practitioners in many different fields of application. The intention has been to provide a consistent summary that may help in selecting an appropriate method for the problem to be solved. It is hoped the extensive bibliography will be of value to researchers.