  Performing global resolvent analysis for high-Reynolds-number turbulent flow
calls for the handling of a large discrete operator. Even though such large
operator is required in the analysis, most applications of resolvent analysis
extracts only a few dominant resolvent response and forcing modes. Here, we
consider the use of randomized numerical linear algebra to reduce the dimension
of the resolvent operator for achieving computational speed up and memory
saving compared to the standard resolvent analysis. To accomplish this goal, we
utilize sketching of the linear operator with random test matrices with a
Gaussian distribution and with insights from the base flow incorporated to
perform singular value decomposition on a low-rank matrix holding dominant
characteristics of the full resolvent operator. The strength of the randomized
resolvent analysis is demonstrated on a turbulent separated flow over an
airfoil. This randomized approach clears the path towards tackling resolvent
analysis for higher-Reynolds number bi- and tri-global base flows.
