  It is a known fact that not all controllable systems can be asymptotically
stabilized by a continuous static feedback. Several approaches have been
developed throughout the last decades, including time-varying, dynamical and
even discontinuous feedbacks. In the latter case, the sample-and-hold framework
is widely used, in which the control input is held constant during sampling
periods. Consequently, only practical stability can be achieved at best.
Existing approaches often require solving optimization problems for finding
stabilizing control actions exactly. In practice, each optimization routine has
a finite accuracy which might influence the state convergence. This work shows,
what bounds on optimization accuracy are required to achieve prescribed
stability margins. Simulation studies support the claim that optimization
accuracy has high influence on the state convergence.
