Concentration of random determinants and permanent estimators

Concentration of permanent estimators for certain large matrices

Let A_n=(a_{ij})_{i,j=1}^n be an n\times n positive matrix with entries in [a,b], 0<a\le b. Let X_n=(\sqrta_{ij}x_{ij})_{i,j=1}^n be a random matrix, where {x_{ij}} are i.i.d. N(0,1) random variables. We show that for large n, \det (X_n^TX_n) concentrates sharply at the permanent of A_n, in the sense that n^{-1}\log (\det(X_n^TX_n)/perA_n)\to_{n\to\infty}0 in probability.

New Permanent Estimators via Non-Commutative Determinants

We introduce a new notion of the determinant, called symmetrized determinant, for a square matrix with the entries in an associative algebra $Å$. The monomial expansion of the symmetrized determinant is obtained from the standard expansion of the commutative determinant by averaging the products of entries of the matrix in all possible orders. We show that for any fixed finite-dimensional associative algebra $Å$, the symmetrized determinant of an $n\times n$ matrix with the entries in $Å$ can be computed in polynomial in $n$ time (the degree of the polynomial is linear in the dimension of $Å$)…

永久性秩与随机矩阵在有限域上的行列式秩

Permanental rank versus determinantal rank of random matrices over finite fields

研究随机矩阵在有限域上永久性秩与行列式秩的区别，发现永久性秩概率与零列概率相似，而行列式秩概率则不同。

Concentration of Lipschitz functionals of determinantal and other strong Rayleigh measures

Let X_1 ,..., X_n be a collection of binary valued random variables and let f : {0,1}^n -> R be a Lipschitz function. Under a negative dependence hypothesis known as the {\em strong Rayleigh} condition, we show that f - E f satisfies a concentration inequality generalizing the classical Gaussian concentration inequality for sums of independent Bernoullis: P (S_n - E S_n > a) < exp (-2 a^2 / n). The class of strong Rayleigh measures includes determinantal measures, weighted uniform matroids and exclusion measures; some familiar examples from these classes are generalized negative binomials and …

The expected characteristic and permanental polynomials of the random Gram matrix

A t by n random matrix A is formed by sampling n independent random column vectors, each containing t components. The random Gram matrix of size n, G_n, contains the dot products between all pairs of column vectors in the randomly generated matrix A; that is, G_n = transpose(A) A. The matrix G_n has characteristic roots coinciding with the singular values of A. Furthermore, the sequences det(G_i) and per(G_i) (for i = 0, 1, ..., n) are factors that comprise the expected coefficients of the characteristic and permanental polynomials of G_n. We prove theorems that relate the generating functions…

Permanent of random matrices from representation theory: moments, numerics, concentration, and comments on hardness of boson-sampling

Computing the distribution of permanents of random matrices has been an outstanding open problem for several decades. In quantum computing, "anti-concentration" of this distribution is an unproven input for the proof of hardness of the task of boson-sampling. We study permanents of random i.i.d. complex Gaussian matrices, and more broadly, submatrices of random unitary matrices. Using a hybrid representation-theoretic and combinatorial approach, we prove strong lower bounds for all moments of the permanent distribution. We provide substantial evidence that our bounds are close to being tight a…