Large-Scale Optimization

Design and analysis of modern optimization algorithms for large-scale problems

Large-scale optimization has recently attracted significant attention not only from the optimization community, but also from the machine learning, statistics as well as the signal processing communities. Traditional general purpose optimization tools are inadequate for these problems due to the complexity of the model, the heterogeneity of the data, and most importantly the sheer data size.

Modern large-scale optimization algorithms, especially those that are capable of exploiting problem structures, dealing with distributed, time varying and incomplete data sets, utilizing massively parallel computing and storage infrastructures, have become the workhorse in the big data era.

Selected Publications

  • Mingyi Hong , Meisam Razaviyayn, Zhi-Quan Luo and Jong-Shi Pang, “A Unified Algorithmic Framework for Block-Structured Optimization Involving Big Data”, IEEE Signal Processing Magazine (* equal contribution), Vol. 33, No. 1, pages 57 - 77, Jan. 2016. Feature Article
  • Mingyi Hong, Zhi-Quan Luo and Meisam Razaviyayn, “Convergence Analysis of Alternating Direction Method of Multipliers for a Family of Nonconvex Problems”, SIAM Journal on Optimization, Vol. 26, No 1, pages 337 - 364, 2016. Finalist, Best Paper Prize for Young Researchers in Continuous Optimization, 2016
  • Meisam Razaviyayn, Mingyi Hong and Zhi-Quan Luo, “A Unified Convergence Analysis of Block Successive Minimization Methods for Nonsmooth Optimization”, SIAM Journal on Optimization. Vol. 23, No. 2, pp. 1126-1153, 2013. Finalist, Best Paper Prize for Young Researchers in Continuous Optimization, 2013