The　positive　semidefinite　constraint　and　equality　constraint　arise　widely　in　matrix　optimization　problems　of　different　areas　including　signal/image　processing,　finance　and　risk　management.　In　this　paper,　an　inexact　accelerated　Augmented　Lagrangian　Method　(ALM)　relying　on　a　parameter　m　is　designed　to　solve　the　structured　low-rank　minimization　with　equality　constraint,　which　is　more　general　and　flexible　than　the　existing　ALM　and　its　variants.　We　prove　a　worst-case　O(1/k(2))　convergence　rate　of　the　new　method　in　terms　of　the　residual　of　the　Lagrangian　function,　and　we　analyze　that　when　m　is　an　element　of　[0,　1)　the　residual　of　our　method　is　smaller　than　that　of　the　traditional　accelerated　ALM.　Compared　with　several　state-of-the-art　methods,　preliminary　numerical　experiments　on　solving　the　Q-weighted　low-rank　correlation　matrix　problem　from　finance　validate　the　efficiency　of　the　proposed　method.　(C)　2017　Elsevier　B.V.　All　rights　reserved.