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The [[Brunn–Minkowski theorem|Brunn–Minkowski inequality]] asserts that the [[Lebesgue measure]] is log-concave. The restriction of the Lebesgue measure to any [[convex set]] is also log-concave.
The [[Brunn–Minkowski theorem|Brunn–Minkowski inequality]] asserts that the [[Lebesgue measure]] is log-concave. The restriction of the Lebesgue measure to any [[convex set]] is also log-concave.


By a theorem of Borell,<ref>{{cite paper | author=Borell, C. | title=Convex set functions in ''d''-space | year = 1975 | mr=0404559|journal=Period. Math. Hungar. |volume=6|issue=2|pages=111–136|doi=10.1007/BF02018814}}</ref> a measure is log-concave if and only if it has a density with respect to the Lebesgue measure on some affine hyperplane, and this density is a [[logarithmically concave function]]. Thus, any [[Gaussian measure]] is log-concave.
By a theorem of Borell,<ref>{{cite journal | author=Borell, C. | title=Convex set functions in ''d''-space | year = 1975 | mr=0404559|journal=Period. Math. Hungar. |volume=6|issue=2|pages=111–136|doi=10.1007/BF02018814| s2cid=122121141 }}</ref> a measure is log-concave if and only if it has a density with respect to the Lebesgue measure on some affine hyperplane, and this density is a [[logarithmically concave function]]. Thus, any [[Gaussian measure]] is log-concave.


The [[Prékopa–Leindler inequality]] shows that a [[convolution]] of log-concave measures is log-concave.
The [[Prékopa–Leindler inequality]] shows that a [[convolution]] of log-concave measures is log-concave.

Revision as of 05:45, 27 January 2022

In mathematics, a Borel measure μ on n-dimensional Euclidean space is called logarithmically concave (or log-concave for short) if, for any compact subsets A and B of and 0 < λ < 1, one has

where λ A + (1 − λB denotes the Minkowski sum of λ A and (1 − λB.[1]

Examples

The Brunn–Minkowski inequality asserts that the Lebesgue measure is log-concave. The restriction of the Lebesgue measure to any convex set is also log-concave.

By a theorem of Borell,[2] a measure is log-concave if and only if it has a density with respect to the Lebesgue measure on some affine hyperplane, and this density is a logarithmically concave function. Thus, any Gaussian measure is log-concave.

The Prékopa–Leindler inequality shows that a convolution of log-concave measures is log-concave.

See also

References

  1. ^ Prékopa, A. (1980). "Logarithmic concave measures and related topics". Stochastic programming (Proc. Internat. Conf., Univ. Oxford, Oxford, 1974). London-New York: Academic Press. pp. 63–82. MR 0592596.
  2. ^ Borell, C. (1975). "Convex set functions in d-space". Period. Math. Hungar. 6 (2): 111–136. doi:10.1007/BF02018814. MR 0404559. S2CID 122121141.