Gradient Descent Provably Solves Nonlinear Tomographic Reconstruction
- DOI
- 10.1109/tit.2026.3677729
- Published
- 2026-05
- Container
- IEEE Transactions on Information Theory
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1109/tit.2026.3677729,
title = {Gradient Descent Provably Solves Nonlinear Tomographic Reconstruction},
author = {Sara Fridovich-Keil and Fabrizio Valdivia and Gordon Wetzstein and Benjamin Recht and Mahdi Soltanolkotabi},
year = {2026},
journal = {IEEE Transactions on Information Theory},
doi = {10.1109/tit.2026.3677729},
url = {https://doi.org/10.1109/tit.2026.3677729}
}RIS
TY - JOUR TI - Gradient Descent Provably Solves Nonlinear Tomographic Reconstruction AU - Sara Fridovich-Keil AU - Fabrizio Valdivia AU - Gordon Wetzstein AU - Benjamin Recht AU - Mahdi Soltanolkotabi PY - 2026 JO - IEEE Transactions on Information Theory DO - 10.1109/tit.2026.3677729 UR - https://doi.org/10.1109/tit.2026.3677729 ER -
APA
Fridovich-Keil, S., Valdivia, F., Wetzstein, G., Recht, B., & Soltanolkotabi, M. (2026). Gradient Descent Provably Solves Nonlinear Tomographic Reconstruction. IEEE Transactions on Information Theory. https://doi.org/10.1109/tit.2026.3677729
Source records
- crossref · retrieved 2026-11-10T11:31:16.965Z