Name
Title | ||
|---|---|---|
An Efficient Quasi-Newton Method For Nonlinear Inverse Problems Via Learned Singular Values |
Abstract | ||
|---|---|---|
Solving complex optimization problems in engineering and the physical sciences requires repetitive computation of multi-dimensional function derivatives, which commonly require computationally-demanding numerical differentiation such as perturbation techniques. In particular, Gauss-Newton methods are used for nonlinear inverse problems that require iterative updates to be computed from the Jacobian and allow for flexible incorporation of prior knowledge. Computationally more efficient alternatives are Quasi-Newton methods, where the repeated computation of the Jacobian is replaced by an approximate update, but unfortunately are often too restrictive for highly ill-posed problems. To overcome this limitation, we present a highly efficient data-driven Quasi-Newton method applicable to nonlinear inverse problems, by using the singular value decomposition and learning a mapping from model outputs to the singular values to compute the updated Jacobian. Enabling time-critical applications and allowing for flexible incorporation of prior knowledge necessary to solve ill-posed problems. We present results for the highly non-linear inverse problem of electrical impedance tomography with experimental data. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/LSP.2021.3063622 | IEEE SIGNAL PROCESSING LETTERS |
Keywords | DocType | Volume |
Jacobian matrices, Artificial neural networks, Inverse problems, Training, Computational modeling, Electrical impedance tomography, Conductivity, Quasi-Newton method, nonlinear inverse problems, neural networks, electrical impedance tomography | Journal | 28 |
ISSN | Citations | PageRank |
1070-9908 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Danny Smyl | 1 | 3 | 2.08 |
| Tyler N. Tallman | 2 | 0 | 0.34 |
| Dong Liu | 3 | 12 | 5.34 |
| Andreas Hauptmann | 4 | 18 | 3.62 |