Zeroth-Order Optimization for Composite Problems with Functional Constraints


  • Zichong Li Rensselaer Polytechnic Institute
  • Pin-Yu Chen IBM Research
  • Sijia Liu Michigan State University
  • Songtao Lu IBM Research
  • Yangyang Xu Rensselaer Polytechnic Institute



Machine Learning (ML)


In many real-world problems, first-order (FO) derivative evaluations are too expensive or even inaccessible. For solving these problems, zeroth-order (ZO) methods that only need function evaluations are often more efficient than FO methods or sometimes the only options. In this paper, we propose a novel zeroth-order inexact augmented Lagrangian method (ZO-iALM) to solve black-box optimization problems, which involve a composite (i.e., smooth+nonsmooth) objective and functional constraints. This appears to be the first work that develops an iALM-based ZO method for functional constrained optimization and meanwhile achieves query complexity results matching the best-known FO complexity results up to a factor of variable dimension. With an extensive experimental study, we show the effectiveness of our method. The applications of our method span from classical optimization problems to practical machine learning examples such as resource allocation in sensor networks and adversarial example generation.




How to Cite

Li, Z., Chen, P.-Y., Liu, S., Lu, S., & Xu, Y. (2022). Zeroth-Order Optimization for Composite Problems with Functional Constraints. Proceedings of the AAAI Conference on Artificial Intelligence, 36(7), 7453-7461.



AAAI Technical Track on Machine Learning II