Optimization plays an important role in solving many inverse problems. Indeed, the task of inversion often either involves or is fully cast as a solution of an optimization problem. In this light, the mere non-linear, non-convex, and large-scale nature of many of these inversions gives rise to some very challenging optimization problems. The inverse problem community has long been developing various techniques for solving such optimization tasks. However, other, seemingly disjoint communities, such as that of machine learning, have developed, almost in parallel, interesting alternative methods which might have stayed under the radar of the inverse problem community. In this survey, we aim to change that. In doing so, we first discuss current state-of-the-art optimization methods widely used in inverse problems. We then survey recent related advances in addressing similar challenges in problems faced by the machine learning community, and discuss their potential advantages for solving inverse problems. By highlighting the similarities among the optimization challenges faced by the inverse problem and the machine learning communities, we hope that this survey can serve as a bridge in bringing together these two communities and encourage cross fertilization of ideas.
|Title of host publication||2017 MATRIX Annals|
|Editors||David R Wood, Jan de Gier, Cheryl E Praeger, Terence Tao|
|Place of Publication||Cham Switzerland|
|Number of pages||20|
|Publication status||Published - 2019|
|Name||MATRIX Book Series|
|Publisher||Springer Nature Switzerland|
Ye, N., Roosta-Khorasani, F., & Cui, T.
(2019). Optimization methods for inverse problems
. In D. R. Wood, J. de Gier, C. E. Praeger, & T. Tao (Eds.), 2017 MATRIX Annals
(Vol. 2, pp. 121-140). (MATRIX Book Series; Vol. 2). Cham Switzerland: Springer. https://doi.org/10.1007/978-3-030-04161-8_9