Semidefinite approximation bound for a class of nonhomogeneous nonconvex quadratically constrained quadratic programming problem

Original Paper


In this paper, we consider a class of nonconvex nonhomogeneous quadratically constrained quadratic optimization problem. We derive some sufficient condition for the input data, and then establish a semi-definite approximation bound based on a randomization algorithm. The approximation bound is optimal in the order of m in general under the given restriction on the input data.


Quadratically constrained quadratic programming Semidefinite programming relaxation Approximation algorithm 



This research was supported by National Natural Science Foundation of China under Grants 11571221.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of MathematicsShanghai UniversityShanghaiPeople’s Republic of China

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