52 research outputs found
Geometric rescaling algorithms for submodular function minimization
We present a new class of polynomial-time algorithms for submodular function minimization (SFM) as well as a unified framework to obtain strongly polynomial SFM algorithms. Our algorithms are based on simple iterative methods for the minimum-norm problem, such as the conditional gradient and Fujishige–Wolfe algorithms. We exhibit two techniques to turn simple iterative methods into polynomial-time algorithms. First, we adapt the geometric rescaling technique, which has recently gained attention in linear programming, to SFM and obtain a weakly polynomial bound O((n4 · EO + n5)log(nL)). Second, we exhibit a general combinatorial black box approach to turn εL-approximate SFM oracles into strongly polynomial exact SFM algorithms. This framework can be applied to a wide range of combinatorial and continuous algorithms, including pseudo-polynomial ones. In particular, we can obtain strongly polynomial algorithms by a repeated application of the conditional gradient or of the Fujishige–Wolfe algorithm. Combined with the geometric rescaling technique, the black box approach provides an O((n5 · EO + n6)log2n) algorithm. Finally, we show that one of the techniques we develop in the paper can also be combined with the cutting-plane method of Lee et al., yielding a simplified variant of their O(n3log2n · EO + n4logO(1)n) algorithm
Online Subset Selection using -Core with no Augmented Regret
We consider the problem of sequential sparse subset selections in an online
learning setup. Assume that the set consists of distinct elements. On
the round, a monotone reward function which assigns a non-negative reward to each subset of is
revealed to a learner. The learner selects (perhaps randomly) a subset of elements before the reward function for that round
is revealed . As a consequence of its choice, the learner receives
a reward of on the round. The learner's goal is to
design an online subset selection policy to maximize its expected cumulative
reward accrued over a given time horizon. In this connection, we propose an
online learning policy called SCore (Subset Selection with Core) that solves
the problem for a large class of reward functions. The proposed SCore policy is
based on a new concept of -Core, which is a generalization of the
notion of Core from the cooperative game theory literature. We establish a
learning guarantee for the SCore policy in terms of a new performance metric
called -augmented regret. In this new metric, the power of the offline
benchmark is suitably augmented compared to the online policy. We give several
illustrative examples to show that a broad class of reward functions, including
submodular, can be efficiently learned with the SCore policy. We also outline
how the SCore policy can be used under a semi-bandit feedback model and
conclude the paper with a number of open problems
- …