优化与大数据：一项调查
Optimization meets Big Data: A survey
摘要：
本文回顾了大数据优化的最新进展，提供了这一新兴领域的最新进展。本文的主要重点是在大数据分析环境中应用的优化技术。回顾和讨论了非线性规划、坐标下降法、交替方向乘子法、模拟优化和元算法，如进化和遗传算法、粒子群优化、差分进化、烟花、蝙蝠、萤火虫和布谷鸟搜索算法的实现。讨论了大数据优化与信息工作流风格、软件架构和软件框架等软件工程主题之间的关系。重点介绍了在大数据优化环境中使用的平台的比较分析，以提供最先进的可能架构和拓扑。
英文摘要：
This paper reviews recent advances in big data optimization, providing the state-of-art of this emerging field. The main focus in this review are optimization techniques being applied in big data analysis environments. Integer linear programming, coordinate descent methods, alternating direction method of multipliers, simulation optimization and metaheuristics like evolutionary and genetic algorithms, particle swarm optimization, differential evolution, fireworks, bat, firefly and cuckoo search algorithms implementations are reviewed and discussed. The relation between big data optimization and software engineering topics like information work-flow styles, software architectures, and software framework is discussed. Comparative analysis in platforms being used in big data optimization environments are highlighted in order to bring a state-or-art of possible architectures and topologies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
