## Abstract

Given a non-negative \(n \times m\) real matrix *A*, the *matrix scaling* problem is to determine if it is possible to scale the rows and columns so that each row and each column sums to a specified positive target values. The Sinkhorn–Knopp algorithm is a simple and classic procedure which alternately scales all rows and all columns to meet these targets. The focus of this paper is the worst-case theoretical analysis of this algorithm. We present an elementary convergence analysis for this algorithm that improves upon the previous best bound. In a nutshell, our approach is to show (i) a simple bound on the number of iterations needed so that the KL-divergence between the current row-sums and the target row-sums drops below a specified threshold \(\delta \), and (ii) then show that for a suitable choice of \(\delta \), whenever KL-divergence is below \(\delta \), then the \(\ell _1\)-error or the \(\ell _2\)-error is below \(\varepsilon \). The well-known Pinsker’s inequality immediately allows us to translate a bound on the KL divergence to a bound on \(\ell _1\)-error. To bound the \(\ell _2\)-error in terms of the KL-divergence, we establish a new inequality, referred to as (**KL vs **\(\ell _1/\ell _2\)). This inequality is a strengthening of Pinsker’s inequality and may be of independent interest.

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## Notes

- 1.
Computationally, this asymptotic viewpoint is unavoidable in the sense that there are simple examples for which the unique matrix scaling matrices need to have irrational entries. For instance, consider the following example from Rothblum and Schneider [32]. The matrix is \(\begin{bmatrix} 1 &{} 1 \\ 1 &{} 2 \end{bmatrix}\) with \({\mathbf {r}}\equiv {\mathbf {c}}\equiv [1,1]^\top \). The unique (up to scaling)

*R*and*S*matrices are \(\begin{bmatrix} (\sqrt{2}+1)^{-1} &{} 0 \\ 0 &{} (\sqrt{2}+2)^{-1} \end{bmatrix}\) and \(\begin{bmatrix} \sqrt{2} &{} 0 \\ 0 &{} 1 \end{bmatrix}\), respectively, giving \(RAS = \begin{bmatrix} 2 - \sqrt{2} &{} \sqrt{2} - 1 \\ \sqrt{2} - 1 &{} 2 - \sqrt{2} \end{bmatrix}\). - 2.
- 3.
[14] never make the base of the logarithm explicit, but their proof shows it can be as large as \(1 - 1/\nu ^2\).

- 4.
The KL-divergence is normally stated between two distributions and doesn’t have the 1/

*h*factor. Also the logarithms are usually base 2.

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## Acknowledgements

We thank Daniel Dadush for asking the connection between our inequality and Hellinger distance, and Jonathan Weed for letting us know of [4]. We would also like to thank the anonymous reviewers for insightful suggestions.

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The second author’s work was supported in part by the National Science Foundation Grants CCF-1552909 and CCF-1617851. A preliminary version of this work was presented in the 1st Symposium on Simplicity in Algorithms, January 10th, 2018, co-located with SODA 2018 in New Orleans.

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Chakrabarty, D., Khanna, S. Better and simpler error analysis of the Sinkhorn–Knopp algorithm for matrix scaling.
*Math. Program.* **188, **395–407 (2021). https://doi.org/10.1007/s10107-020-01503-3

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### Keywords

- Matrix scaling
- Alternate minimization
- KL divergence
- Matchings

### Mathematics Subject Classification

- 68Q25
- 68W40