Invertibility via distance for non-centered random matrices with continuous distributions

Let $A$ be an $n\times n$ random matrix with independent rows $R_1(A),\dots ,R_n(A)$, and assume that for any $i\le n$ and any three-dimensional linear subspace $F\subset {\mathbb{R}}^n$ the orthogonal projection of $R_i(A)$ onto $F$ has distribution density $\rho(x):F\to{\mathbb R}_+$satisfying$\rho(x)\leq C_1/\max(1,|x|_2^{2000})$($x\in F$) for some constant $C_1>0$. We show that for any fixed $n\times n$ real matrix $M$ we have $$\mathbb{P}\{s_{\min }(A+M)\le t{n}^{-1\mathrm{/}2}\}\le C^{\mathrm{\prime }}t,t>0,$$ where $C^{\mathrm{\prime }}>0$ is a universal constant. In particular, the above result holds if the rows of $A$ are independent centered log-concave random vectors with identity covariance matrices. Our method is free from any use of covering arguments, and is principally different from a standard approach involving a decomposition of the unit sphere and coverings, as well as an approach of Sankar-Spielman-Teng for non-centered Gaussian matrices.