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ICASSP

2011

IEEE

2011

IEEE

Finding the least squares (LS) solution s to a system of linear equations Hs = y where H, y are given and s is a vector of binary variables, is a well known NP-hard problem. In this paper, we consider binary LS problems under the assumption that the coef cient matrix H is also unknown, and lies in a given uncertainty ellipsoid. We show that the corresponding worst-case robust optimization problem, although NP-hard, is still amenable to semide nite relaxation (SDR)-based approximations. However, the relaxation step is not obvious, and requires a certain problem reformulation to be ef cient. The proposed relaxation is motivated using Lagrangian duality and simulations suggest that it performs well, offering a robust alternative over the traditional SDR approaches for binary LS problems.

Related Content

Added |
21 Aug 2011 |

Updated |
21 Aug 2011 |

Type |
Journal |

Year |
2011 |

Where |
ICASSP |

Authors |
Efthymios Tsakonas, Joakim Jalden, Björn E. Ottersten |

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