Performance enhancements for a generic conic interior point algorithm (2107.04262v2)

Abstract: In recent work, we provide computational arguments for expanding the class of proper cones recognized by conic optimization solvers, to permit simpler, smaller, more natural conic formulations. We define an exotic cone as a proper cone for which we can implement a small set of tractable (i.e. fast, numerically stable, analytic) oracles for a logarithmically homogeneous self-concordant barrier for the cone or for its dual cone. Our extensible, open source conic interior point solver, Hypatia, allows modeling and solving any conic optimization problem over a Cartesian product of exotic cones. In this paper, we introduce Hypatia's interior point algorithm. Our algorithm is based on that of Skajaa and Ye [2015], which we generalize by handling exotic cones without tractable primal oracles. With the goal of improving iteration count and solve time in practice, we propose a sequence of four enhancements to the interior point stepping procedure of Skajaa and Ye [2015]: (1) loosening the central path proximity condition, (2) adjusting the directions using a third order directional derivative barrier oracle, (3) performing a backtracking search on a curve, and (4) combining the prediction and centering directions. We implement 23 useful exotic cones in Hypatia. We summarize the complexity of computing oracles for these cones, showing that our new third order oracle is not a bottleneck, and we derive efficient and numerically stable oracle implementations for several cones. We generate a diverse benchmark set of 379 conic problems from 37 different applied examples. Our computational testing shows that each stepping enhancement improves Hypatia's iteration count and solve time. Altogether, the enhancements reduce the shifted geometric means of iteration count and solve time by over 80% and 70% respectively.