Solving Hankel matrix approximation problem using semidefinite programming
Copyright policy )Solving Hankel matrix approximation problem using semidefinite programming
The author studies the problem of finding the nearest matrix to a positive semidefinite Hankel matrix in such a way that all the special properties are preserved. In the usual evaluation techniques rounding and truncation limitations result in some of the properties to be lost. The author describes a semidefinite programming optimization problem, the solution of which can produce a suitable matrix. The proposed algorithm is presented in detail (section 4) and the results of the computational experimentation are reported at the end of the article.
- King Fahd University of Petroleum and Minerals Saudi Arabia
nearest matrix, numerical examples, algorithm, Applied Mathematics, Primal–dual interior-point method, Other matrix algorithms, Hankel matrix, Interior-point methods, semidefinite programming, primal-dual interior-point algorithm, Computational Mathematics, Numerical mathematical programming methods, Semidefinite programming
nearest matrix, numerical examples, algorithm, Applied Mathematics, Primal–dual interior-point method, Other matrix algorithms, Hankel matrix, Interior-point methods, semidefinite programming, primal-dual interior-point algorithm, Computational Mathematics, Numerical mathematical programming methods, Semidefinite programming
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