  We consider the weight design problem for the consensus algorithm under a
finite time horizon. We assume that the underlying network is random where the
links fail at each iteration with certain probability and the link failures can
be spatially correlated. We formulate a family of weight design criteria
(objective functions) that minimize n, n = 1,...,N (out of N possible) largest
(slowest) eigenvalues of the matrix that describes the mean squared consensus
error dynamics. We show that the objective functions are convex; hence,
globally optimal weights (with respect to the design criteria) can be
efficiently obtained. Numerical examples on large scale, sparse random networks
with spatially correlated link failures show that: 1) weights obtained
according to our criteria lead to significantly faster convergence than the
choices available in the literature; 2) different design criteria that
corresponds to different n, exhibits very interesting tradeoffs: faster
transient performance leads to slower long time run performance and vice versa.
Thus, n is a valuable degree of freedom and can be appropriately selected for
the given time horizon.
