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