Convex quartic problems: homogenized gradient method and preconditioning

TL;DR: In this article , the authors consider a convex minimization problem for which the objective is the sum of a homogeneous polynomial of degree four and a linear term, and they design a first-order method called Homogenized Gradient, along with an accelerated version, which enjoy fast convergence rates.

Abstract: We consider a convex minimization problem for which the objective is the sum of a homogeneous polynomial of degree four and a linear term. Such task arises as a subproblem in algorithms for quadratic inverse problems with a difference-of-convex structure. We design a first-order method called Homogenized Gradient, along with an accelerated version, which enjoy fast convergence rates of respectively $\mathcal{O}(\kappa^2/K^2)$ and $\mathcal{O}(\kappa^2/K^4)$ in relative accuracy, where $K$ is the iteration counter. The constant $\kappa$ is the quartic condition number of the problem. Then, we show that for a certain class of problems, it is possible to compute a preconditioner for which this condition number is $\sqrt{n}$, where $n$ is the problem dimension. To establish this, we study the more general problem of finding the best quadratic approximation of an $\ell_p$ norm composed with a quadratic map. Our construction involves a generalization of the so-called Lewis weights.