2008
DOI: 10.1016/j.jpaa.2007.05.022
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A tropical approach to secant dimensions

Abstract: Tropical geometry yields good lower bounds, in terms of certain combinatorial-polyhedral optimisation problems, on the dimensions of secant varieties. The approach is especially successful for toric varieties such as Segre-Veronese embeddings. In particular, it gives an attractive pictorial proof of the theorem of Hirschowitz that all Veronese embeddings of the projective plane except for the quadratic one and the quartic one are non-defective; and indeed, no Segre-Veronese embeddings are known where the tropi… Show more

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Cited by 68 publications
(93 citation statements)
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“…This case has been covered previously in Draisma's tropical approach to secant dimensions [17]. The corresponding implications for the dimension of (not tropical) mixtures of independence models have also been studied in algebraic geometry and tensor analysis; see [10,2,26].…”
Section: Definitionmentioning
confidence: 89%
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“…This case has been covered previously in Draisma's tropical approach to secant dimensions [17]. The corresponding implications for the dimension of (not tropical) mixtures of independence models have also been studied in algebraic geometry and tensor analysis; see [10,2,26].…”
Section: Definitionmentioning
confidence: 89%
“…The generalization to arbitrary visible hierarchical models is left for future work. Furthermore, similarly to [17], the tropical approach leads in many cases to combinatorial conditions that can be very difficult to verify outside of well-established cardinality bounds for error correcting codes. We think that a promising direction is the formulation of dimension bounds in terms of simpler models, as done in Lemma 25 for the case of a binary independence hidden model.…”
Section: Now Note That Ifmentioning
confidence: 99%
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“…This can be taken either as a definition of Trop(X ) or as a theorem when other definitions are chosen [9,11,19,20]. Indeed, in [19] it is proved that X an is the projective limit of the tropicalisations Trop(X ) for all choices of coordinates.…”
Section: Introductionmentioning
confidence: 99%