Well-conditioned Primal-Dual Interior-point Method for Accurate Low-rank Semidefinite Programming

TL;DR

This paper introduces a well-conditioned reformulation of the Newton subproblem in primal-dual interior-point methods for low-rank semidefinite programming, significantly improving efficiency and accuracy at high precision levels.

Contribution

It proposes a novel well-conditioned reformulation of the Newton subproblem that maintains bounded condition number and reduces computational costs for high-accuracy SDP solutions.

Findings

Reduces per-iteration cost to O(n^3) time and O(n^2) memory.

Maintains bounded condition number throughout all iterations.

Preconditioner improves convergence of inner iterations in practice.

Abstract

We describe how the low-rank structure in an SDP can be exploited to reduce the per-iteration cost of a convex primal-dual interior-point method down to $O(n^{3})$ time and $O(n^{2})$ memory, even at very high accuracies. A traditional difficulty is the dense Newton subproblem at each iteration, which becomes progressively ill-conditioned as progress is made towards the solution. Preconditioners have been proposed to improve conditioning, but these can be expensive to set up, and fundamentally become ineffective at high accuracies, as the preconditioner itself becomes increasingly ill-conditioned. Instead, we present a well-conditioned reformulation of the Newton subproblem that is cheap to set up, and whose condition number is guaranteed to remain bounded over all iterations of the interior-point method. In theory, applying an inner iterative method to the reformulation reduces the per-iteration cost of the outer interior-point method to $O(n^{3})$ time and $O(n^{2})$ memory. We also present a well-conditioned preconditioner that theoretically increases the outer per-iteration cost to $O(n^{3}r^{3})$ time and $O(n^{2}r^{2})$ memory, where $r$ is an upper-bound on the solution rank, but in practice greatly improves the convergence of the inner iterations.