Solving Conic Systems via Projection and Rescaling

We propose a simple projection and rescaling algorithm to solve the feasibility problem $$\text{ find } x \in L \cap \Omega,$$ where $L$ and $\Omega$ are respectively a linear subspace and the interior of a symmetric cone in a finite-dimensional vector space $V$. This projection and rescaling algorithm is inspired by previous work on rescaled versions of the perceptron algorithm and by Chubanov's projection-based method for linear feasibility problems. As in these predecessors, each main iteration of our algorithm contains two steps: a {\em basic procedure} and a {\em rescaling} step. When $L \cap \Omega \ne \emptyset$, the projection and rescaling algorithm finds a point $x \in L \cap \Omega$ in at most $O(\log(1/\delta(L \cap \Omega)))$ iterations, where $\delta(L \cap \Omega) \in (0,1]$ is a measure of the most interior point in $L \cap \Omega$. The ideal value $\delta(L\cap \Omega) = 1$ is attained when $L \cap \Omega$ contains the center of the symmetric cone $\Omega$. We describe several possible implementations for the basic procedure including a perceptron scheme and a smooth perceptron scheme. The perceptron scheme requires $O(r^4)$ perceptron updates and the smooth perceptron scheme requires $O(r^2)$ smooth perceptron updates, where $r$ stands for the Jordan algebra rank of $V$.