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Robust Algorithms for Online Convex Problems via Primal-Dual
Primal-dual methods in online optimization give several of the state-of-the
art results in both of the most common models: adversarial and
stochastic/random order. Here we try to provide a more unified analysis of
primal-dual algorithms to better understand the mechanisms behind this
important method. With this we are able of recover and extend in one goal
several results of the literature.
In particular, we obtain robust online algorithm for fairly general online
convex problems: we consider the MIXED model where in some of the time steps
the data is stochastic and in the others the data is adversarial. Both the
quantity and location of the adversarial time steps are unknown to the
algorithm. The guarantees of our algorithms interpolate between the (close to)
best guarantees for each of the pure models. In particular, the presence of
adversarial times does not degrade the guarantee relative to the stochastic
part of the instance.
Concretely, we first consider Online Convex Programming: at each time a
feasible set is revealed, and the algorithm needs to select
to minimize the total cost , for a convex function .
Our robust primal-dual algorithm for this problem on the MIXED model recovers
and extends, for example, a result of Gupta et al. and recent work on
-norm load balancing by the author. We also consider the problem of
Welfare Maximization with Convex Production Costs: at each time a customer
presents a value and resource consumption vector , and the goal is
to fractionally select customers to maximize the profit . Our robust primal-dual algorithm on the MIXED model
recovers and extends the result of Azar et al.
Given the ubiquity of primal-dual algorithms we hope the ideas presented here
will be useful in obtaining other robust algorithm in the MIXED or related
models