[02298] New semidefinite relaxations for a class of complex quadratic programming problems
Session Time & Room : 3C (Aug.23, 13:20-15:00) @A201
Type : Contributed Talk
Abstract : In this talk, we propose some new semidefinite relaxations for a class of nonconvex complex quadratic programming problems, widely appear in signal processing and power system. By deriving new valid constraints to the matrix variables in the lifted space, we derive some enhanced semidefinite relaxations of complex quadratic programming problems. Then, we compare the proposed semidefinite relaxations with existing ones, and show that the newly proposed semidefinite relaxations could be strictly tighter than the previous ones. Numerical results indicate that the proposed semidefinite relaxations not only provide tighter relaxation bounds but also improve some existing approximation algorithms by finding better sub-optimal solutions.