Online Optimization with Uncertain Information

  • Authors:
  • Mohammad Mahdian;Hamid Nazerzadeh;Amin Saberi

  • Affiliations:
  • Yahoo! Research;Microsoft Research;Stanford University

  • Venue:
  • ACM Transactions on Algorithms (TALG)
  • Year:
  • 2012

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Abstract

We introduce a new framework for designing online algorithms that can incorporate additional information about the input sequence, while maintaining a reasonable competitive ratio if the additional information is incorrect. Within this framework, we present online algorithms for several problems including allocation of online advertisement space, load balancing, and facility location.