论文标题
NCVX：用于约束机器和深度学习的通用优化求解器
NCVX: A General-Purpose Optimization Solver for Constrained Machine and Deep Learning
论文作者
论文摘要
施加明确的约束是相对较新的，但在深度学习中越来越紧迫，例如，值得信赖的AI对复杂的扰动集和需要尊重物理法律和约束的科学应用进行了强大的优化。但是，如果没有优化的专业知识，可能很难可靠地解决限制的深度学习问题。现有的深度学习框架不承认限制。通用优化软件包可以处理约束，但不能执行自动差异，并且难以处理非平滑度。在本文中，我们介绍了一个名为NCVX的新软件包，其初始版本包含求解器Pygranso，这是一个启用Pytorch的通用通用优化软件包，用于约束机器/深度学习问题，这是第一个同类问题。 NCVX继承了Pytorch的自动差异，GPU加速度和张量变量，并建立在自由使用且广泛使用的开源框架上。 NCVX可在https://ncvx.org上找到，并提供详细的文档以及来自机器/深度学习和其他领域的众多示例。
Imposing explicit constraints is relatively new but increasingly pressing in deep learning, stimulated by, e.g., trustworthy AI that performs robust optimization over complicated perturbation sets and scientific applications that need to respect physical laws and constraints. However, it can be hard to reliably solve constrained deep learning problems without optimization expertise. The existing deep learning frameworks do not admit constraints. General-purpose optimization packages can handle constraints but do not perform auto-differentiation and have trouble dealing with nonsmoothness. In this paper, we introduce a new software package called NCVX, whose initial release contains the solver PyGRANSO, a PyTorch-enabled general-purpose optimization package for constrained machine/deep learning problems, the first of its kind. NCVX inherits auto-differentiation, GPU acceleration, and tensor variables from PyTorch, and is built on freely available and widely used open-source frameworks. NCVX is available at https://ncvx.org, with detailed documentation and numerous examples from machine/deep learning and other fields.
