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This work studies several questions about the optimality of semidefinite programming (SDP) for constraint satisfaction problems (CSPs). First we propose the hypothesis that the well known Basic SDP relaxation is actually optimal for random instances of constraint satisfaction problems for every predicate. This unifies several conjectures proposed in the past, and suggests a unifying principle for the average-case complexity of CSPs. We provide several types of indirect evidence for the truth ofdoi:10.1145/2422436.2422460 dblp:conf/innovations/BarakKS13 fatcat:k3omfqtzq5h7hgx5p3qpzabgpi