Operations Research Transactions

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Preemptive online algorithms for scheduling

DING Chao1,*   

  1. 1.  Academy of Mathematics and Systems Science,  Chinese Academy of Sciences, Beijing 100190, China
  • Received:2017-08-15 Online:2017-12-15 Published:2017-12-15

Abstract:

Matrix optimization problems (MOPs) have been recognized in recent years to be a powerful tool to model many important applications arising from emerging fields such as data science  {within and beyond the optimization community}. Perturbation analysis of optimization problems play a fundamental and crucial role in optimization, which provided important theoretical foundation for algorithm designing and others. Science MOPs are non-polyhedral, the corresponding analysis is totally different from that of the classical polyhedral case (e.g., the nonlinear programming). Basing on results obtained in [1,2], we summary the recent progress on perturbation analysis of MOPs.

Key words: matrix optimization, perturbation analysis, robustly isolated calmness, calmness, metric subregularity