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We propose an interior point method (IPM) for solving semidefiniteprogramming problems (SDPs). The standard interior point algorithms used tosolve SDPs work in the space of positive semidefinite matrices. Contrary tothat the proposed algorithm works in the cone of matrices of constant\emph{factor width}. This adaptation makes the proposed method more suitablefor parallelization than the standard IPM. We prove global convergence andprovide a complexity analysis. Our work is inspired by a series of papers byAhmadi, Dash, Majumdar and Hall, and builds upon a recent preprint byRoig-Solvas and Sznaier [arXiv:2202.12374, 2022].

A predictor-corrector algorithm for semidefinite programming that uses the factor width cone