Skip to content
Preprint

A central limit theorem for the random assignment problem

Aug 2026 · 1 citation · 35 references
Mathematics

Abstract

Let \(C_n\) be the minimum cost of a perfect matching in an \(n\times n\) matrix of independent uniform random variables. We prove that \[ \sqrt n\{C_n-\zeta(2)\} \ \Longrightarrow\ \mathcal N\bigl(0,4\zeta(2)-4\zeta(3)\bigr). \] The proof begins with an exact change of variables based on a uniformly rooted shortest-path selection of an optimal dual potential. After the unused reduced costs are integrated out, a reference law separates the rows conditionally on the potential field, while the ordered potential gaps become independent exponentials. The only residual dependence is a directed-tree factor. Ordering the potentials turns its zero--one support into a Ferrers matrix, whose matrix-tree determinant is triangular. A singular inverse-degree estimate and exact normalization then yield total-variation convergence to the reference law. Finally, a conditional triangular-array central limit theorem accounts for row noise, and a second triangular array accounts for the linear response of the potential field. The strategy used here is likely to be applicable to other problems.

View source

Similar papers

Preprint Aug 2026

Sharp Tail Bounds Beyond Twice the Mean

Consider $n$ independent, non-negative, mean at most one random variables, $X_1,X_2,\ldots$. We show the following bound on the probability of their sum exceeding a threshold $t$: \[ \mathbb{P}\left[\sum_{i=1}^n X_i\ge t\right] \leq 1-\left(1-\frac{1}{t}\right)^n \text{ for all } t\ge 2n+1 \,. \] To prove this, we cons...

P. Strack, Jannik M. Westermann · 1 citation
Preprint Aug 2026

Strong Convergence for a General Class of Random Matrix Models

Let \(X_{1,n},\ldots,X_{d,n}\) be \(n\times n\) random matrices built from independent i.i.d. entry arrays, with centered entries, normalized by \(n^{-1/2}\). We prove that, if every entry law has finite fourth moment, then this tuple converges almost surely strongly in \(*\)-distribution to a free circular family with...

Yan-Jin Xiang, Zhi-Hua Zhang · 1 citation
Preprint Aug 2026

Factorial residues modulo a prime: beyond the square-root bound

For a prime \(p\), let \(A_p=\{k!\pmod p:1\leq k<p\}\). We prove \(|A_p|\gg p^{8/15}\), improving the general lower bound \((\sqrt{2}-o(1))p^{1/2}\). The proof begins with the identity \((n+2)!=(n+1)!+((n+1)!)^2/n!\) in \(\mathbb{F}_p\), which produces many incidences for a family of fractional-linear maps. After Cauch...

Xi-Yu Hu · 1 citation
Preprint Aug 2026

A Central Limit Theorem for Regularized M-Estimators

We prove a quantitative central limit theorem for linear functionals of regularized empirical-risk minimizers in the proportional-dimensional regime \(p=O(n)\). The data columns are independent, not necessarily identically distributed, and satisfy a uniform columnwise Poincar\'e inequality. Under uniform curvature and...

Cosme Louart · 1 citation
Preprint Aug 2026

A Near-Optimal Lower Bound for Prefix-Matrix Factorizations

For the $n\times n$ lower-triangular all-ones matrix $Q$, we prove a near-optimal lower bound \[ \gamma_{2,1}(Q) := \inf_{Q=AB} \|A\|_{2\to\infty}\|B\|_{1\to1} = \Omega\!\left( \frac{\log^{3/2}n}{(\log\log n)^{3/2}} \right), \] where the infimum ranges over real factorizations of arbitrary finite inner dimension. This...

Hong-Hao Lin, V. Mirrokni, David P. Woodruff · 2 citations
Preprint Sep 2026

Free-Probabilistic State Evolution and Random Matrix Discrepancy

Let $A_1,\ldots,A_n$ be independent $d \times d$ real symmetric Gaussian random matrices, and consider the linear operator $A(x) = n^{-1/2}\sum_{i=1}^n x_i A_i$, $x\in \mathbb{R}^n$. We construct an iterative algorithm in the Approximate Message Passing family which iterates over $A$ and its adjoint $A^*$, and establis...

August Y. Chen, A. El Alaoui · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.