Most existing work in online stochastic combinatorial optimization assumes that inputs are drawn from independent distributions -- a strong assumption that often fails in practice. At the other extreme, arbitrary correlations are equivalent to worst-case inputs via Yao's minimax principle, making good algorithms often impossible. This motivates the study of intermediate models that capture mild correlations while still permitting non-trivial algorithms. In this paper, we study online combinatorial optimization under Markov Random Fields (MRFs), a well-established graphical model for structured dependencies. MRFs parameterize correlation strength via the maximum weighted degree Δ, smoothly interpolating between independence (Δ= 0) and full correlation (Δ). While naïvely this yields e^O (Δ) -competitive algorithms and Ω (Δ) hardness, we ask: when can we design tight Θ (Δ) -competitive algorithms? We present general techniques achieving O (Δ) -competitive algorithms for both minimization and maximization problems under MRF-distributed inputs. For minimization problems with coverage constraints (e. g. , Facility Location and Steiner Tree), we reduce to the well-studied p-sample model. For maximization problems (e. g. , matchings and combinatorial auctions with XOS buyers), we extend the "balanced prices" framework for online allocation problems to MRFs.
Gao et al. (Mon,) studied this question.