A Class of Weighted Low Rank Approximation of the Positive Semidefinite Hankel Matrix
Copyright policy )A Class of Weighted Low Rank Approximation of the Positive Semidefinite Hankel Matrix
We consider the weighted low rank approximation of the positive semidefinite Hankel matrix problem arising in signal processing. By using the Vandermonde representation, we firstly transform the problem into an unconstrained optimization problem and then use the nonlinear conjugate gradient algorithm with the Armijo line search to solve the equivalent unconstrained optimization problem. Numerical examples illustrate that the new method is feasible and effective.
- Guilin University of Electronic Technology China (People's Republic of)
Signal theory (characterization, reconstruction, filtering, etc.), numerical examples, Numerical mathematical programming methods, semidefinite Hankel matrix, Nonlinear programming, nonlinear conjugate gradient algorithm, Armijo line search, QA1-939, unconstrained optimization, signal processing, Mathematics
Signal theory (characterization, reconstruction, filtering, etc.), numerical examples, Numerical mathematical programming methods, semidefinite Hankel matrix, Nonlinear programming, nonlinear conjugate gradient algorithm, Armijo line search, QA1-939, unconstrained optimization, signal processing, Mathematics
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