New semidefinite relaxations for a class of complex quadratic programming problems

better sub-optimal solutions. [5/5 of https://t.co/HcshZ7RU6u]

previous ones are only designed for special cases. Numerical results indicate that the proposed semidefinite relaxations not only provide tighter relaxation bounds but also improve some existing approximation algorithms by finding [4/5 of https://t.co/Hcsh

newly proposed semidefinite relaxations could be strictly tighter than the previous ones. Moreover, the proposed semidefinite relaxations can be applied to more general cases of complex quadratic programming problems, whereas the [3/5 of https://t.co/HcshZ

the matrix variables in the lifted space, we derive some enhanced semidefinite relaxations of the complex quadratic programming problems. Then, we compare the proposed semidefinite relaxations with existing ones and show that the [2/5 of https://t.co/HcshZ