The 2-INTERVAL PATTERN problem is to find the largest constrained pattern in a set of 2-intervals. The constrained pattern is a subset of the given 2-intervals such that any pair of them are R-comparable, where model $$R \subseteq \{ <, \sqsubset, \mathtt{(\hspace{-3.5pt})} \}$$ . The problem stems from the study of general representation of RNA secondary structures. In this paper, we give three improved algorithms for different models. Firstly, an O(n{log} n +L) algorithm is proposed for the case $$R= \{ \mathtt{(\hspace{-3.5pt})} \} $$ , where $${\cal L}=O(dn)=O(n^2)$$ is the total length of all 2-intervals (density d is the maximum number of 2-intervals over any point). This improves previous O(n 2log n) algorithm. Secondly, we use dynamic programming techniques to obtain an O(nlog n + dn) algorithm for the case R = { <, ⊏ }, which improves previous O(n 2) result. Finally, we present another $$O(n {\rm log} n +{\cal L})$$ algorithm for the case $$R = \{\sqsubset, \mathtt{(\hspace{-3.5pt})} \}$$ with disjoint support(interval ground set), which improves previous O(n 2□n) upper bound.