Solving semidefinite–quadratic–linear programs using SDPT3

This paper discusses computational experiments with linear optimization problems involving semi-definite, quadratic, and linear cone constraints (SQLPs). Many test problems of this type are solved using a new release of SDPT3, a Matlab implementation of infeasible primal–dual path-following algorithms. The software developed by the authors uses Mehrotra-type predictor–corrector variants of interior-point methods and two types of search directions: the HKM and NT directions. A discussion of implementation details is provided and computational results on problems from the SDPLIB and DIMACS Challenge collections are reported.