A note on convex relaxations for the inverse eigenvalue problem
Author:
Funder
National Science Foundation
Air Force Office of Scientific Research
Alfred P. Sloan Foundation
Publisher
Springer Science and Business Media LLC
Subject
Control and Optimization
Link
https://link.springer.com/content/pdf/10.1007/s11590-021-01708-1.pdf
Reference26 articles.
1. Ahmadi, A.A., Majumdar, A.: DSOS and SDSOS optimization: more tractable alternatives to sum of squares and semidefinite optimization. SIAM J. Appl. Algebra Geom. 3(2), 193–230 (2019)
2. Blekherman, G., Parrilo, P. A., Thomas, R.R.: Semidefinite Oand Convex Algebraic Geometry, vol. 13. SIAM (2013)
3. Boley, D., Golub, G.H.: A survey of matrix inverse eigenvalue problems. Inverse Probl. 3(4), 595 (1987)
4. Candogan, U.O., Chandrasekaran, V.: Finding planted subgraphs with few eigenvalues using the Schur–Horn relaxation. SIAM J. Optim. 28(1), 735–759 (2018)
5. Candogan, U.O., Chandrasekaran, V.: Convex graph invariant relaxations for graph edit distance. arXiv preprint arXiv:1904.08934 (2019)
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