A Probabilistic Framework for Learnable Optimization Algorithms

Excerpt

arXiv:2408.11629v2 Announce Type: replace Abstract: We propose a statistical-learning framework for optimization algorithms. The framework is based on probability distributions over optimization trajectories induced by a distribution of optimization problems and a learnable optimization algorithm. Within this setting, optimization performance is represented through measurable performance functionals, including stopping times, contraction factors, and trajectory-level properties. The resulting fr