  Devising efficient algorithms to solve continuously-varying strongly convex
optimization programs is key in many applications, from control systems to
signal processing and machine learning. In this context, solving means to find
and track the optimizer trajectory of the continuously-varying convex
optimization program. Recently, a novel prediction-correction methodology has
been put forward to set up iterative algorithms that sample the
continuously-varying optimization program at discrete time steps and perform a
limited amount of computations to correct their approximate optimizer with the
new sampled problem and predict how the optimizer will change at the next time
step. Prediction-correction algorithms have been shown to outperform more
classical strategies, i.e., correction-only methods. Typically,
prediction-correction methods have asymptotic tracking errors of the order of
$h^2$, where $h$ is the sampling period, whereas classical strategies have
order of $h$. Up to now, Prediction-correction algorithms have been developed
in the primal space, both for unconstrained and simply constrained convex
programs. In this paper, we show how to tackle linearly constrained
continuously-varying problem by prediction-correction in the dual space and we
prove similar asymptotic error bounds as their primal versions.
