An $O(1/T^3)$ algorithm for minimizing convex quadratic functions over the $L_1$ ball

We study convex quadratic minimization over the unit $L_1$ ball in which the maximum eigenvalue of the Hessian matrix is bounded by a positive constant $L$. We propose a novel first-order algorithm with objective value error bounded by $O(L/T^3)$ after $T$ gradient evaluations, assuming that the subproblems involved in the algorithm can be solved exactly. To the best of our knowledge, the best convergence rate of algorithms in the literature is $O(L/T^2)$. From the perspective of information-based complexity theory, our proposed algorithm is the first in the literature that achieves the $O((L/\varepsilon)^{1/3})$ first-order oracle complexity, although its current version is not necessarily practical for implementation. We hope that our proposed algorithm could shed some light on future implementable and efficient $O(L/T^3)$-convergence-rate algorithms. The proposed algorithm incorporates a decomposition of components of vectors in the unit $L_1$-norm ball to "good" and "bad" parts, and uses symmetric rank-1 (SR1) updates on the bad parts. The proposed algorithm was developed after the author instructed the OpenAI ChatGPT 6 (Astra) model to study the problem using ideas of weak-type $L^1$ estimates and good-bad part decomposition in harmonic analysis and a recent result.