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This letter provides a tractable bound for a perfect recovery condition in compressed sensing matrices using the spherical section property in the presence of side information. In particular, when the signal of interest is provided with side-information, we derive an equivalent semidefinite relaxation bound by introducing the related prior knowledge as an additional constraint to the semidefinite programming (SDP) problem. We recast a linear program (LP) cone to this problem and found the dual-SDP to be less complex to handle. Numerical evaluations on the proposed dual-SDP, validates the existence of sparse solutions with high- cardinalities.