On the formulation and theory of the Newton interior-point method for nonlinear programming
Journal of Optimization Theory and Applications
Conjugate Gradient Methods for Toeplitz Systems
SIAM Review
Linear and Nonlinear Image Deblurring: A Documented Study
SIAM Journal on Numerical Analysis
On the Newton interior-point method for nonlinear programming problems
Journal of Optimization Theory and Applications
Warm-Start Strategies in Interior-Point Methods for Linear Programming
SIAM Journal on Optimization
Nonlinear image restoration using FFT-based conjugate gradient methods
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol.2)-Volume 2 - Volume 2
A Nonnegatively Constrained Convex Programming Method for Image Reconstruction
SIAM Journal on Scientific Computing
Efficient Minimization Methods of Mixed l2-l1 and l1-l1 Norms for Image Restoration
SIAM Journal on Scientific Computing
Covariance-Preconditioned Iterative Methods for Nonnegatively Constrained Astronomical Imaging
SIAM Journal on Matrix Analysis and Applications
Preconditioned Iterative Methods for Weighted Toeplitz Least Squares Problems
SIAM Journal on Matrix Analysis and Applications
Iterative methods for the reconstruction of astronomical images with high dynamic range
Journal of Computational and Applied Mathematics - Special issue: Applied computational inverse problems
Inner solvers for interior point methods for large scale nonlinear programming
Computational Optimization and Applications
A nonmonotone semismooth inexact Newton method
Optimization Methods & Software
Restoration of images based on subspace optimization accelerating augmented Lagrangian approach
Journal of Computational and Applied Mathematics
A hybrid multilevel-active set method for large box-constrained linear discrete ill-posed problems
Calcolo: a quarterly on numerical analysis and theory of computation
Spectral signal unmixing with interior-point nonnegative matrix factorization
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
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Nonlinear image deblurring procedures based on probabilistic considerations have been widely investigated in the literature. This approach leads to model the deblurring problem as a large scale optimization problem, with a nonlinear, convex objective function and non-negativity constraints on the sign of the variables. The interior point methods have shown in the last years to be very reliable in nonlinear programs. In this paper we propose an inexact Newton interior point (IP) algorithm designed for the solution of the deblurring problem. The numerical experience compares the IP method with another state-of-the-art method, the Lucy Richardson algorithm, and shows a significant improvement of the processing time.