A fast algorithm for solving regularized total least squares problems.
A homotopy approach to rational covariance extension with degree constraint
The solutions to the Rational Covariance Extension Problem (RCEP) are parameterized by the spectral zeros. The rational filter with a specified numerator solving the RCEP can be determined from a known convex optimization problem. However, this optimization problem may become ill-conditioned for some parameter values. A modification of the optimization problem to avoid the ill-conditioning is proposed and the modified problem is solved efficiently by a continuation method.
A parameter choice method for Tikhonov regularization.
Filter factors of truncated TLS regularization with multiple observations
The total least squares (TLS) and truncated TLS (T-TLS) methods are widely known linear data fitting approaches, often used also in the context of very ill-conditioned, rank-deficient, or ill-posed problems. Regularization properties of T-TLS applied to linear approximation problems were analyzed by Fierro, Golub, Hansen, and O’Leary (1997) through the so-called filter factors allowing to represent the solution in terms of a filtered pseudoinverse of applied to . This paper focuses on the situation...
-curve curvature bounds via Lanczos bidiagonalization.
Linear least squares with quadratic constraints.
Noise propagation in regularizing iterations for image deblurring.
Numerical linear algebra for nonlinear microwave imaging.
On a modified Kovarik algorithm for symmetric matrices.
On the choice of subspace for iterative methods for linear discrete ill-posed problems
Many iterative methods for the solution of linear discrete ill-posed problems with a large matrix require the computed approximate solutions to be orthogonal to the null space of the matrix. We show that when the desired solution is not smooth, it may be possible to determine meaningful approximate solutions with less computational work by not imposing this orthogonality condition.
Quasi-boundary value method for non-well posed problem for a parabolic equation with integral boundary condition.
Regularization of nonlinear ill-posed equations with accretive operators.
Regularized nonnegative matrix factorization: geometrical interpretation and application to spectral unmixing
Residual norm behavior for Hybrid LSQR regularization
Hybrid LSQR represents a powerful method for regularization of large-scale discrete inverse problems, where ill-conditioning of the model matrix and ill-posedness of the problem make the solutions seriously sensitive to the unknown noise in the data. Hybrid LSQR combines the iterative Golub-Kahan bidiagonalization with the Tikhonov regularization of the projected problem. While the behavior of the residual norm for the pure LSQR is well understood and can be used to construct a stopping criterion,...
Simple square smoothing regularization operators.
Tikhonov regularization with nonnegativity constraint.
Truncated spectral regularization for an ill-posed non-linear parabolic problem
It is known that the nonlinear nonhomogeneous backward Cauchy problem , with , where is a densely defined positive self-adjoint unbounded operator on a Hilbert space, is ill-posed in the sense that small perturbations in the final value can lead to large deviations in the solution. We show, under suitable conditions on and , that a solution of the above problem satisfies an integral equation involving the spectral representation of , which is also ill-posed. Spectral truncation is used...