2010
DOI: 10.2140/involve.2009.2.611
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Proof of the planar double bubble conjecture using metacalibration methods

Abstract: We prove the double bubble conjecture in ‫ޒ‬ 2 : that the standard double bubble in ‫ޒ‬ 2 is boundary length-minimizing among all figures that separately enclose the same areas. Our independent proof is given using the new method of metacalibration, a generalization of traditional calibration methods useful in minimization problems with fixed volume constraints.MSC2000: 49Q05, 49Q10, 53A10.

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Cited by 5 publications
(6 citation statements)
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“…The first proof of this result for n = 2 was given in [10] exploiting the analysis carried out in [22]. A second proof appeared in [8]. The case n = 3 was established first in [13] for equal volumes and then in [14] with no restrictions.…”
mentioning
confidence: 98%
“…The first proof of this result for n = 2 was given in [10] exploiting the analysis carried out in [22]. A second proof appeared in [8]. The case n = 3 was established first in [13] for equal volumes and then in [14] with no restrictions.…”
mentioning
confidence: 98%
“…• On R n -Long believed to be true, but appearing explicitly as a conjecture in an undergraduate thesis by J. Foisy in 1991 [16], the Euclidean double-bubble problem in R n was considered in the 1990's by various authors. In the Euclidean plane R 2 , the conjecture was confirmed in [17] (see also [39,14,9]). In R 3 , the equal volumes case v 1 = v 2 was settled in [22,23], and the structure of general double-bubbles was further studied in [25].…”
Section: Previously Known and Related Resultsmentioning
confidence: 78%
“…Below we list some of the previously known results regarding the above three conjectures, and refer to the excellent book by F. Morgan [38,Chapters 13,14,18,19] for additional information.…”
Section: Previously Known and Related Resultsmentioning
confidence: 99%
“…It is well known that the double bubble is the unique such minimizer (see e.g. [8,18] for the 2D case, and [12] for the 3D case, and also [22,7,16,17]). In the ternary 3D case, however, such simplification is not available, and the shape of the minimizers is unclear, even for small masses.…”
Section: Setting Up the Problemmentioning
confidence: 99%