## Abstract

We consider semidefinite programming relaxation (SDR) of a binary quadratic minimization problem. This NP-hard problem arises naturally in the maximum-likelihood detection of discrete signals for digital communications. We analyze the average performance of the SDR algorithm for a class of randomly generated binary quadratic minimization problems. Although the SDR worst-case approximation ratio is unbounded for this NP-hard problem, our analysis shows that SDR can provide in polynomial time a provably near-optimal solution, achieving a constant factor approximation of the optimal objective value in probability. Moreover, this constant factor remains bounded with increasing problem size. Our proof is based on an asymptotic analysis of Karush-Kuhn-Tucker optimality conditions using random matrix theory.

Original language | English (US) |
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Pages (from-to) | 1906-1922 |

Number of pages | 17 |

Journal | SIAM Journal on Optimization |

Volume | 20 |

Issue number | 4 |

DOIs | |

State | Published - Apr 29 2010 |

## Keywords

- Binary quadratic minimization
- Probabilistic performance analysis
- Random matrix theory
- Semidefinite relaxation