We propose a new method for linear second-order cone programs. It is based on
the sequential quadratic programming framework for nonlinear programming. In contrast to interior
point methods, it can capitalize on the warm-start capabilities of active-set quadratic programming
subproblem solvers and achieve a local quadratic rate of convergence.
In order to overcome the non-differentiability or singularity observed in nonlinear formulations of
the conic constraints, the subproblems approximate the cones with polyhedral outer approximations
that are refined throughout the iterations. For nondegenerate instances, the algorithm implicitly
identifies the set of cones for which the optimal solution lies at the extreme points. As a consequence,
the final steps are identical to regular sequential quadratic programming steps for a differentiable
nonlinear optimization problem, yielding local quadratic convergence.
We prove the global and local convergence guarantees of the method and present numerical
experiments that confirm that the method can take advantage of good starting points and can
achieve higher accuracy compared to a state-of-the-art interior point solver.