39,382 research outputs found
Scheduling commercial advertisements for television
The problem of scheduling the commercial advertisements in the television industry is investigated. Each advertiser client demands that the multiple airings of the same brand advertisement should be as spaced as possible over a given time period. Moreover, audience rating requests have to be taken into account in the scheduling. This is the first time this hard decision problem is dealt with in the literature. We design two mixed integer linear programming (MILP) models. Two constructive heuristics, local search procedures and simulated annealing (SA) approaches are also proposed. Extensive computational experiments, using several instances of various sizes, are performed. The results show that the proposed MILP model which represents the problem as a network flow obtains a larger number of optimal solutions and the best non-exact procedure is the one that uses SA
Bounds on Query Convergence
The problem of finding an optimum using noisy evaluations of a smooth cost
function arises in many contexts, including economics, business, medicine,
experiment design, and foraging theory. We derive an asymptotic bound E[ (x_t -
x*)^2 ] >= O(1/sqrt(t)) on the rate of convergence of a sequence (x_0, x_1,
>...) generated by an unbiased feedback process observing noisy evaluations of
an unknown quadratic function maximised at x*. The bound is tight, as the proof
leads to a simple algorithm which meets it. We further establish a bound on the
total regret, E[ sum_{i=1..t} (x_i - x*)^2 ] >= O(sqrt(t)) These bounds may
impose practical limitations on an agent's performance, as O(eps^-4) queries
are made before the queries converge to x* with eps accuracy.Comment: 6 pages, 2 figure
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