Towards designing learned optimization algorithms that are usable beyond
their training setting, we identify key principles that classical algorithms
obey, but have up to now, not been used for Learning to Optimize (L2O).
Following these principles, we provide a general design pipeline, taking into
account data, architecture and learning strategy, and thereby enabling a
synergy between classical optimization and L2O, resulting in a philosophy of
Learning Optimization Algorithms. As a consequence our learned algorithms
perform well far beyond problems from the training distribution. We demonstrate
the success of these novel principles by designing a new learning-enhanced BFGS
algorithm and provide numerical experiments evidencing its adaptation to many
settings at test time.

通过识别经典算法服从的关键原则并将其用于优化学习（L2O）中，我们提供了一个综合设计流程，以数据、架构和学习策略为考虑因素，从而实现经典优化与 L2O 之间的协同，形成了学习优化算法的理念。通过设计一种新的增强学习 BFGS 算法并提供数值实验证明其在多种测试环境中的适应性，我们展示了这些新原则的成功。