  In practical computations, the (preconditioned) conjugate gradient (P)CG
method is the iterative method of choice for solving systems of linear
algebraic equations $Ax=b$ with a real symmetric positive definite matrix $A$.
During the iterations it is important to monitor the quality of the approximate
solution $x_k$ so that the process could be stopped whenever $x_k$ is accurate
enough. One of the most relevant quantities for monitoring the quality of $x_k$
is the squared $A$-norm of the error vector $x-x_k$. This quantity cannot be
easily evaluated, however, it can be estimated. Many of the existing estimation
techniques are inspired by the view of CG as a procedure for approximating a
certain Riemann--Stieltjes integral. The most natural technique is based on the
Gauss quadrature approximation and provides a lower bound on the quantity of
interest. The bound can be cheaply evaluated using terms that have to be
computed anyway in the forthcoming CG iterations. If the squared $A$-norm of
the error vector decreases rapidly, then the lower bound represents a tight
estimate. In this paper we suggest a heuristic strategy aiming to answer the
question of how many forthcoming CG iterations are needed to get an estimate
with the prescribed accuracy. Numerical experiments demonstrate that the
suggested strategy is efficient and robust.
