The Method of Ellipcenters for Strongly Convex Functions

The Method of Ellipcenters (ME), introduced in~\cite{ME2025} for strongly convex quadratic minimization, uses two gradient evaluations per iteration: one at the current iterate and one at a companion point on the same level set.

We extend ME to the broader class of strongly convex functions with Lipschitz continuous gradient.

We prove that ME contracts unconditionally at the linear rate $1-\mu^2/L^2$, and that at every step where the two gradient directions are linearly independent, which, in dimension at least two, is every step generically, it matches the rate of gradient descent with exact line search.

In that linearly independent case, a midpoint argument exploiting the level-set symmetry yields a further per-step improvement, which is global when the angle between the two gradients is uniformly bounded away from zero.

The same symmetry forces this angle to be obtuse, so the improvement is strictly active at every such step.

ME also converges in at most two steps in dimension two.

Numerical experiments on regularized logistic regression confirm the theoretical predictions.