  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.
