The articles in this special section focus on nonconvex optimization for signal processing and machine learning. Optimization is now widely recognized as an indispensable tool in signal processing (SP) and machine learning (ML). Indeed, many of the advances in these fields rely crucially on the formulation of suitable optimization models and deployment of efficient numerical optimization algorithms. In the early 2000s, there was a heavy focus on the use of convex optimization techniques to tackle SP and ML applications. This is largely due to the fact that convex optimization problems often possess favorable theoretical and computational properties and that many problems of practical interest have been shown to admit convex formulations or good convex approximations.

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信号处理和机器学习的非凸优化 [来自客座编辑]

此特殊部分中的文章侧重于信号处理和机器学习的非凸优化。优化现在被广泛认为是信号处理 (SP) 和机器学习 (ML) 中不可或缺的工具。事实上，这些领域的许多进展关键依赖于合适优化模型的制定和有效数值优化算法的部署。在 2000 年代初期，人们非常关注使用凸优化技术来解决 SP 和 ML 应用程序。这主要是因为凸优化问题通常具有有利的理论和计算特性，并且许多实际感兴趣的问题已被证明允许凸公式或良好的凸近似。