An Approximation Algorithm for Solving Standard Quadratic Optimization Problems

What is it about?

This paper describes an approximate algorithm of finding inner optimal values of StQPs. The approximate algorithm fuzzifies variable x in Rn with normalized possibility distributions and simplifies the solving of StQPs. The approximation ratio is discussed and determined. Numerical results show that (1) the new algorithm achieves higher accuracy than semidefinite programming method and linear programming approximation method; (2) the novel algorithm consumes less than one out of fourth computational time that is consumed by linear programming approximation method; (3) the computational time of the new algorithm does not correlate with the matrix densities whereas the computational times of the branch-and-bound and heuristic algorithms do.

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Why is it important?

Compare with existing algorithms, the algorithm is fast and able to achieve accurate solutions.

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