A Stable Numerical Method for Inverting Shape from Moments
SIAM Journal on Scientific Computing
Block-Toeplitz/Hankel Structured Total Least Squares
SIAM Journal on Matrix Analysis and Applications
Approximation of Large-Scale Dynamical Systems (Advances in Design and Control) (Advances in Design and Control)
Exact and Approximate Modeling of Linear Systems: A Behavioral Approach (Mathematical Modeling and Computation) (Mathematical Modeling and Computation)
Proceedings of the 2006 international symposium on Symbolic and algebraic computation
Survey paper: Structured low-rank approximation and its applications
Automatica (Journal of IFAC)
A new algebra of Toeplitz-plus-Hankel matrices and applications
Computers & Mathematics with Applications
Programming collective intelligence
Programming collective intelligence
Optimization Algorithms on Matrix Manifolds
Optimization Algorithms on Matrix Manifolds
Exact Matrix Completion via Convex Optimization
Foundations of Computational Mathematics
High-performance numerical algorithms and software for structured total least squares
Journal of Computational and Applied Mathematics
A Singular Value Thresholding Algorithm for Matrix Completion
SIAM Journal on Optimization
Low Rank Approximation: Algorithms, Implementation, Applications
Low Rank Approximation: Algorithms, Implementation, Applications
Reconstructing polygons from moments with connections to arrayprocessing
IEEE Transactions on Signal Processing
A Gauss-Newton-like optimization algorithm for“weighted” nonlinear least-squares problems
IEEE Transactions on Signal Processing
The geometry of weighted low-rank approximations
IEEE Transactions on Signal Processing
Invariants and canonical forms for systems structural and parametric identification
Automatica (Journal of IFAC)
| Hi-index | 7.29 |
A software package is presented that computes locally optimal solutions to low-rank approximation problems with the following features: *mosaic Hankel structure constraint on the approximating matrix, *weighted 2-norm approximation criterion, *fixed elements in the approximating matrix, *missing elements in the data matrix, and *linear constraints on an approximating matrix's left kernel basis. It implements a variable projection type algorithm and allows the user to choose standard local optimization methods for the solution of the parameter optimization problem. For an mxn data matrix, with nm, the computational complexity of the cost function and derivative evaluation is O(m^2n). The package is suitable for applications with n@?m. In statistical estimation and data modeling-the main application areas of the package-n@?m corresponds to modeling of large amount of data by a low-complexity model. Performance results on benchmark system identification problems from the database DAISY and approximate common divisor problems are presented.