We present an object-oriented optimization framework that can be employed to solve small- and large-scale problems based on the concept of vectors and operators. By using such a strategy, we implement different iterative optimization algorithms that can be used in combination with architecture-independent vectors and operators, allowing the minimization of single-machine or cluster-based problems with a unique codebase. We implement a Python library following the described structure with a user-friendly interface that is designed to seamlessly scale to high-performance-computing (HPC) environments. We demonstrate its flexibility and scalability on multiple inverse problems, where convex and non-convex objective functions are optimized with different iterative algorithms.

我们提出了一个面向对象的优化框架，该框架可用于基于向量和运算符的概念来解决小型和大型问题。通过使用这种策略，我们实现了不同的迭代优化算法，这些算法可以与与体系结构无关的向量和运算符结合使用，从而通过唯一的代码库将单机或基于集群的问题最小化。我们按照所描述的结构通过用户友好的界面实现Python库，该界面旨在无缝扩展到高性能计算（HPC）环境。我们展示了它在多个反问题上的灵活性和可伸缩性，其中凸和非凸目标函数使用不同的迭代算法进行了优化。