Task Embedded Coordinate Update: A Realizable Framework for Multivariate Non-Convex Optimization


  • Yiyang Wang Fudan University
  • Risheng Liu Dalian University of Technology
  • Long Ma Dalian University of Technology
  • Xiaoliang Song The Hong Kong Polytechnic University




We in this paper propose a realizable framework TECU, which embeds task-specific strategies into update schemes of coordinate descent, for optimizing multivariate non-convex problems with coupled objective functions. On one hand, TECU is capable of improving algorithm efficiencies through embedding productive numerical algorithms, for optimizing univariate sub-problems with nice properties. From the other side, it also augments probabilities to receive desired results, by embedding advanced techniques in optimizations of realistic tasks. Integrating both numerical algorithms and advanced techniques together, TECU is proposed in a unified framework for solving a class of non-convex problems. Although the task embedded strategies bring inaccuracies in sub-problem optimizations, we provide a realizable criterion to control the errors, meanwhile, to ensure robust performances with rigid theoretical analyses. By respectively embedding ADMM and a residual-type CNN in our algorithm framework, the experimental results verify both efficiency and effectiveness of embedding task-oriented strategies in coordinate descent for solving practical problems.




How to Cite

Wang, Y., Liu, R., Ma, L., & Song, X. (2019). Task Embedded Coordinate Update: A Realizable Framework for Multivariate Non-Convex Optimization. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 1650-1657. https://doi.org/10.1609/aaai.v33i01.33011650



AAAI Technical Track: Constraint Satisfaction and Optimization