Functional analysis Normed and Banach spaces Theorem
Grothendieck inequality
Builds on Hilbert spaceInner productProjective tensor product
Statement
There is a constant with the following property. Let and let be a real matrix. Assume that for all . Let be a real Hilbert space and let be vectors of norm at most in . Then
Remarks
The constant does not depend on , or . The smallest such constant is the real Grothendieck constant . Its exact value is unknown. Krivine proved .
One proof uses random Gaussian vectors . For unit vectors and , the average of is . So signs drawn at random from the vectors reproduce a function of the inner products. Krivine first transforms the vectors so that this function becomes a fixed multiple of the inner product.
The inequality has a complex version, with a different constant. In computer science it shows that a semidefinite relaxation approximates the cut norm of a matrix within a constant factor.
Going deeper
For a student
Grothendieck stated the result in his work on tensor products of Banach spaces. In that language it says that every bounded linear map from to is absolutely summing. The inequality is a key tool in the study of the projective tensor product and of operator ideals.
Reading path
To understand this page from the ground up, read these 4 pages in order. The foundations come first.
Show the path as a graph
- no evidence level
- Hilbert space concept
- Inner product concept
- Projective tensor product concept
- Grothendieck inequality statement
Further reading
About this page
| Tag | NX8AMNXWG8 |
|---|---|
| URI | m2://NX8AMNXWG8 |
| Kind | statement, theorem |
| Writing | W0 stub |
| MSC | 46B28, 46M05, 46B07 |
| Status | This copy of the site has no build data. Run m2 status NX8AMNXWG8. |
| About | Hilbert space |
| Uses | Hilbert space, Inner product, Projective tensor product |
| Same as | |
| Changed | 2 November 2026 |
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