  We present a method for fast evaluation of the covariance matrix for a
two-point galaxy correlation function (2PCF) measured with the Landy-Szalay
estimator. The standard way of evaluating the covariance matrix consists in
running the estimator on a large number of mock catalogs, and evaluating their
sample covariance. With large random catalog sizes (data-to-random objects
ratio M>>1) the computational cost of the standard method is dominated by that
of counting the data-random and random-random pairs, while the uncertainty of
the estimate is dominated by that of data-data pairs. We present a method
called Linear Construction (LC), where the covariance is estimated for small
random catalogs of size M = 1 and M = 2, and the covariance for arbitrary M is
constructed as a linear combination of these. We validate the method with
PINOCCHIO simulations in range r = 20-200 Mpc/h, and show that the covariance
estimate is unbiased. With M = 50 and with 2 Mpc/h bins, the theoretical
speed-up of the method is a factor of 14. We discuss the impact on the
precision matrix and parameter estimation, and derive a formula for the
covariance of covariance.
