RI: Small: Efficient Projection-Free Algorithms for Optimization and Online Machine Learning

互联网的出现导致了数据收集、可用性和复杂性的指数级增长。随着它的增加，我们对更有效的数据分析算法的需求也在增加。对于超大规模的数据集，唯一可行的数据分析技术是迭代线性时间一阶优化方法。将这些最先进的迭代方法应用于机器学习和数据分析的计算瓶颈通常是所谓的“投影步骤”。该项目解决了设计无投影优化算法的需求，该算法通过更有效的线性优化步骤取代投影。该项目的一个关键贡献是不断传播和转让这项技术。开源软件的发布将继续支持科学和工程领域的大规模机器学习应用。除了理论和算法之外，该项目更广泛的影响目标还包括编写一本关于机器学习中有效优化技术的教科书，以及开发一门新的课程，重点是为学生准备该领域的科学和工程需求。

The advent of the Internet gives rise to an exponential growth in data collection, availability and complexity. With it increases our need for more efficient data analysis algorithms. Over super-scale datasets, the only feasible data analysis techniques are iterative linear-time first-order optimization methods. The computational bottleneck in applying these state-of-the-art iterative methods to machine learning and data analysis is often the so-called "projection step". This project addresses the need to design projection-free optimization algorithms that replace projections by more efficient linear optimization steps. A key contribution of the project is the continual dissemination and transfer of this technology. The open-source software releases will continue to enable large-scale machine learning applications in science and engineering. The broader impact goals of the project, beyond theory and algorithms, include the development of a textbook on efficient optimization techniques in machine learning, as well as the development of a new curriculum focused on preparing students for the scientific and engineering needs in this field.