Ignace Loris
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Research Subjects
Ignace Loris has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
F. A. Dahlen
Department of Geosciences, Princeton University, Princeton, NJ, USA
2 shared publicationsGuust Nolet
Department of Geosciences, Princeton University, Princeton, NJ, USA
2 shared publicationsIngrid Daubechies
Program in Applied and Computational Mathematics, Princeton University, Princeton, NJ, USA
2 shared publications- Caroline Verhoeven
Mathematics Department Vrije Universiteit Brussel Pleinlaan 2 1050 Brussel Belgium
1 shared publication - Ingrid Daubechies
Department of Mathematics and Program in Applied and Computational Mathematics Princeton University 08544
1 shared publication - Massimo Fornasier
Program in Applied and Computational Mathematics, Princeton University, Fine Hall, Washington Road, 08544-1000 Princeton NJ, USA
1 shared publication
Latest Publications
- Open Access
Practical error estimates for sparse recovery in linear inverse problems
The effectiveness of using model sparsity as a priori information when solving linear inverse problems is studied. We investigate the reconstruction quality of such a method in the non-idealized case and compute some typical recovery errors (depending on the sparsity of the desired solution, the number of data, the noise level on the data, and various properties of the measurement matrix); they are compared to known theoretical bounds and illustrated on a magnetic tomography example.
Open AccessPractical error estimates for sparse recovery in linear inverse problems
The effectiveness of using model sparsity as a priori information when solving linear inverse problems is studied. We investigate the reconstruction quality of such a method in the non-idealized case and compute some typical recovery errors (depending on the sparsity of the desired solution, the number of data, the noise level on the data, and various properties of the measurement matrix); they are compared to known theoretical bounds and illustrated on a magnetic tomography example.
- 17 September 2008
- Published by Springer Nature in Journal of Fourier Analysis and Applications
- Vol. 14 (5-6) , 764-792
Accelerated Projected Gradient Method for Linear Inverse Problems with Sparsity Constraints
- 17 September 2008
- Published by Springer Nature in Journal of Fourier Analysis and Applications
- Vol. 14 (5-6) , 764-792
Accelerated Projected Gradient Method for Linear Inverse Problems with Sparsity Constraints
- Open Access
- 1 July 2007
- Published by Oxford University Press (OUP) in Geophysical Journal International
- Vol. 170 (1) , 359-370
Tomographic inversion using ℓ1-norm regularization of wavelet coefficients
- 1 July 2007
- Published by Oxford University Press (OUP) in Geophysical Journal International
- Vol. 170 (1) , 359-370
Open AccessTomographic inversion using ℓ1-norm regularization of wavelet coefficients
- Open Access
Tomographic inversion using $\ell_1$-norm regularization of wavelet coefficients
We propose the use of $\ell_1$ regularization in a wavelet basis for the solution of linearized seismic tomography problems $Am=d$, allowing for the possibility of sharp discontinuities superimposed on a smoothly varying background. An iterative method is used to find a sparse solution $m$ that contains no more fine-scale structure than is necessary to fit the data $d$ to within its assigned errors.Open AccessTomographic inversion using $\ell_1$-norm regularization of wavelet coefficients
We propose the use of $\ell_1$ regularization in a wavelet basis for the solution of linearized seismic tomography problems $Am=d$, allowing for the possibility of sharp discontinuities superimposed on a smoothly varying background. An iterative method is used to find a sparse solution $m$ that contains no more fine-scale structure than is necessary to fit the data $d$ to within its assigned errors. - 1 May 2004
- Published by Elsevier in Physics Letters A
Quantum vs classical mechanics: role of elementary excitations
- 1 May 2004
- Published by Elsevier in Physics Letters A
Quantum vs classical mechanics: role of elementary excitations