Abstract
Generalized inverses form a set of key tools in matrix algebra. For large-scale applications, sparsity is highly desirable, and so sparse generalized inverses have been studied. One such family is based on relaxing the well-known Moore-Penrose properties. One of those properties is non-linear, and so we develop a convex-programming relaxation and an associated “diving” heuristic to achieve a good trade-off between sparsity and satisfaction of the non-linear Moore-Penrose property.
M. Fampa was supported in part by CNPq grant 303898/2016-0. J. Lee was supported in part by ONR grant N00014-17-1-2296
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Fuentes, V.K., Fampa, M., Lee, J. (2020). Diving for Sparse Partially-Reflexive Generalized Inverses. In: Le Thi, H., Le, H., Pham Dinh, T. (eds) Optimization of Complex Systems: Theory, Models, Algorithms and Applications. WCGO 2019. Advances in Intelligent Systems and Computing, vol 991. Springer, Cham. https://doi.org/10.1007/978-3-030-21803-4_9
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