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SQP algorithms for solving Toeplitz matrix approximation problem
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/9781118723203openalex-snapshot
Practical Methods of Optimization
inferredcites →https://doi.org/10.1007/978-1-4899-6790-9_8openalex-snapshot
Moment Problems and Low Rank Toeplitz Approximations
inferredcites →https://doi.org/10.1007/bf01599017openalex-snapshot
Moment problems and low rank Toeplitz approximations
inferredcites →https://doi.org/10.1016/0005-1098(93)90033-popenalex-snapshot
Discrete random signals and statistical signal processing
inferredcites →https://doi.org/10.1016/0378-4754(88)90129-2openalex-snapshot
Practical methods of optimization
inferredcites →https://doi.org/10.1016/b978-0-12-468662-5.50009-4openalex-snapshot
NUMERICAL EXPERIMENTS WITH AN EXACT L1 PENALTY FUNCTION METHOD
inferredcites →https://doi.org/10.1109/tit.1974.1055174openalex-snapshot
A view of three decades of linear filtering theory
inferredcites →https://doi.org/10.1137/0323032openalex-snapshot
Semi-Definite Matrix Constraints in Optimization
inferredcites →https://doi.org/10.1137/0614052openalex-snapshot
Approximation by a Hermitian Positive Semidefinite Toeplitz Matrix
inferredcites →https://doi.org/10.1137/0614053openalex-snapshot
A Weakly Stable Algorithm for Padé Approximants and the Inversion of Hankel Matrices
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/s10957-007-9254-5openalex-snapshot
Approximate Toeplitz Matrix Problem Using Semidefinite Programming
inferred← citeshttps://doi.org/10.1016/j.csda.2013.10.004openalex-snapshot
Covariance structure regularization via entropy loss function
inferred← citeshttps://doi.org/10.1016/j.patrec.2010.10.011openalex-snapshot
Efficient approximate Regularized Least Squares by Toeplitz matrix
inferred← citeshttps://doi.org/10.1109/acssc.2013.6810371openalex-snapshot
Efficient approximation of structured covariance under joint Toeplitz and rank constraints
inferred← citeshttps://doi.org/10.1109/camsap.2013.6714017openalex-snapshot
Constrained ML estimation of structured covariance matrices with applications in radar STAP
inferred← citeshttps://doi.org/10.1109/maes.2014.140147openalex-snapshot
Student research highlight estimation of structured covariance matrices for radar STAP
inferred← citeshttps://doi.org/10.1109/radar.2014.6875659openalex-snapshot
Estimation of structured covariance matrices for radar STAP under practical constraints
inferred← citeshttps://doi.org/10.1109/taes.2014.130647openalex-snapshot
Computationally efficient toeplitz approximation of structured covariance under a rank constraint
inferred← citeshttps://doi.org/10.1163/156939506776382148openalex-snapshot
Semidefinite and second-order cone optimization approach for the Toeplitz matrix approximation problem
inferred← citeshttps://doi.org/10.2298/fil1703737aopenalex-snapshot
Global convergence of the alternating projection method for the Max-Cut relaxation problem
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