In the first part we study critical points of random polynomials. We choose two deterministic sequences of complex numbers, whose empirical measures converge to the same probability measure in complex plane. We make a sequence of polynomials whose zeros are chosen from either of sequences at random. We show that the limiting empirical measure of zeros and critical points agree for these polynomials. As a consequence we show that when we randomly perturb the zeros of a deterministic sequence of polynomials, the limiting empirical measures of zeros and critical points agree. This result can be interpreted as an extension of earlier results where randomness is reduced. Pemantle and Rivin initiated the study of critical points of random polynomials. Kabluchko proved the result considering the zeros to be i.i.d. random variables.

In the second part we deal with the spectrum of products of Ginibre
matrices. Exact eigenvalue density is known for a very few matrix
ensembles. For the known ones they often lead to determinantal point
process. Let `$X_1,X_2,...,X_k$`

be i.i.d. matrices of size $n \times n$ whose entries
are independent complex Gaussian random variables. We derive the
eigenvalue density for matrices of the form `$Y_1.Y_2....Y_n$`

, where each
`$Y_i = X_i$ or $X_i^{-1}$`

. We show that the eigenvalues form a determinantal
point process. The case where $k=2$, `$Y_1=X_1,Y_2=X_2^{-1}$`

was derived
earlier by Krishnapur. The case where `$Y_i =X_i$`

for all `$i=1,2,...,n$`

, was
derived by Akemann and Burda. These two known cases can be obtained as
special cases of our result.

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Last updated: 13 Sep 2024