Siemens EDA (Mentor) has published their pioneering work on matrix OPC at SPIE before, in the same title but part I and II. Based on this work, an OPC feature MatrixOPC has been developed at Siemens EDA (Mentor). The MatrixOPC feature is now used by customers in production recipes routinely. However, this work was only focused on rectilinear OPC or Manhattan masks. In this paper, we present our current effort in generalizing the rectilinear matrix OPC to the curvilinear mask setting and to curvilinear OPC. Our initial test with a particular test case shows a promise that the new version, curvilinear matrix OPC and still under development, may also become a useful supplemental instrument for our curvilinear OPC solutions, compared to the curvilinear OPC practices without it. In this paper we will define the Jacobian matrix for the curvilinear mask setting, and compare the Jacobian matrices obtained from the brute-force definition and from our fast approximation algorithm, by comparing their total differentials. We also compare the OPC results from regular curvilinear OPC and matrix OPC with a fast approximated Jacobian.