Algorithmic and optimization aspects of Brascamp-Lieb inequalities, via Operator Scaling
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Abstract (arXiv)
The celebrated Brascamp-Lieb (BL) inequalities (and their extensions) are an important mathematical tool, unifying and generalizing numerous inequalities in analysis, convex geometry and information theory. While their structural theory is very well understood, far less is known about computing their main parameters. We give polynomial time algorithms to compute feasibility of BL-datum, the optimal BL-constant and a weak separation oracle for the BL-polytope. The same result holds for the so-called Reverse BL inequalities of Barthe. The best known algorithms for any of these tasks required at least exponential time. The algorithms are obtained by a simple efficient reduction of a given BL-datum to an instance of the Operator Scaling problem defined by Gurvits, for which the present authors have provided a polynomial time algorithm. This reduction implies algorithmic versions of many of the known structural results, and in some cases provide proofs that are different or simpler than existing ones. Of particular interest is the fact that the operator scaling algorithm is continuous in its input. Thus as a simple corollary of our reduction we obtain explicit bounds on the magnitude and continuity of the BL-constant in terms of the BL-data. To the best of our knowledge no such bounds were known, as past arguments relied on compactness. The continuity of BL-constants is important for developing non-linear BL inequalities that have recently found so many applications.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Jul 2016.
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Citing papers per year: 2021: 2; 2022: 2; 2023: 0; 2024: 1.
5 papers.
- 2022A Strongly Polynomial Algorithm for Approximate Forster Transforms and its Application to Halfspace Learningcited by 11
- 2021Optimization and Sampling Under Continuous Symmetry: Examples and Lie Theorycited by 5
- 2021No-go Theorem for Acceleration in the Hyperbolic Planecited by 4
- 2022On a class of geodesically convex optimization problems solved via Euclidean MM methodscited by 1
- 2024Disciplined Geodesically Convex Programmingcited by 1
- 2024Disciplined Geodesically Convex Programming7 Jul 2024
- 2022A Strongly Polynomial Algorithm for Approximate Forster Transforms and its Application to Halfspace Learning6 Dec 2022
- 2022On a class of geodesically convex optimization problems solved via Euclidean MM methods22 Jun 2022
- 2021Optimization and Sampling Under Continuous Symmetry: Examples and Lie Theory2 Sep 2021
- 2021No-go Theorem for Acceleration in the Hyperbolic Plane14 Jan 2021