Searcharxiv⌕ Search

arXiv · 2610.25104

Non-Acceleration of the Heavy-Ball Method in 1D

Abstract

Despite having an asymptotically optimal rate of convergence on convex quadratics, the acceleration of Polyak's heavy-ball method and a characterization of its dynamics within more general settings have remained long standing open problems. Recently, the work of Goujaud et al. [Math. Program. (2025)] made progress regarding acceleration in the context of smooth, strongly convex minimization: for a small enough inverse condition number, either the method does not accelerate on quadratics, or there exists a smooth strongly convex function such that heavy-ball cycles over the roots of unity. The constructed functions have two variables, thus leaving the question of acceleration over univariate functions open. In this work, we use a Fourier-based analysis to i) provide a proof for the non-acceleration of the heavy-ball method over the class of univariate smooth strongly convex functions. We also ii) provide a strict enlargement of the previous root-of-unity cycling region, with insights regarding the "fractional" sets of Goujaud et al. [arXiv:2502.19916], and iii) give a higher dimensional cycling instance which is not covered by root-of-unity cycling.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Konstantinos Fotopoulos, Michael Helcig, Karl Deck, Jannis Alsbach. 2026-10-18. Non-Acceleration of the Heavy-Ball Method in 1D. https://arxiv.org/abs/2610.25104

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

A third order dynamical system for generalized monotone equation

We propose a third order dynamical system for solving a nonlinear equation in Hilbert spaces where the operator is cocoercive with respect to the solutions set. Under mild conditions on the parameters, we establish the existence and uniqueness of the generated trajectories as well as its asymptotic convergence to a solution of the equation. When the operator is strongly monotone with respect to the solutions set, we deliver an exponential convergence rate of $e^{-2t}$, which is significantly faster than the known results of second order dynamical systems. In particular, for convex optimization problems, the proposed dynamical system provides a fast convergence rate of {\bf $\mathcal{O}(\frac{1}{t^3})$} for the objective values. In addition, we discuss the applications of the proposed dynamical system to several splitting monotone inclusion problems.

math.OC↗

Exploring chordal sparsity in semidefinite programming with sparse plus low-rank data matrices

Semidefinite programming (SDP) problems are challenging to solve because of their high dimensionality. However, solving sparse SDP problems with small tree-width are known to be relatively easier because: (1) they can be decomposed into smaller multi-block SDP problems through chordal conversion; (2) they have low-rank optimal solutions. In this paper, we study more general SDP problems whose coefficient matrices have sparse plus low-rank (SPLR) structure. We develop a unified framework to convert such problems into sparse SDP problems with bounded tree-width. Based on this, we derive rank bounds for SDP problems with SPLR structure, which are tight in the worst case.

math.OC↗

Assortment optimization given basket shopping behavior using the Ising model

In markets where customers tend to purchase baskets of products rather than single products, assortment optimization is a major challenge for retailers. Removing a product from a retailer's assortment can result in a severe drop in aggregate demand if this product is a complement to other products. Therefore, accounting for the complementarity effect is essential when making assortment decisions. In this paper, we develop an analytical framework designed to address this problem. We model customers' choices using the binary Ising model, which is a special case of a Markov random field. This model captures pairwise demand dependencies as well as the individual attractiveness of each product, with binary variables indicating whether products are included in the purchased basket. For estimation, we utilize existing methods developed for the spin formulation of the Ising model and then apply a parameter transformation to express the fitted model in the binary form used for assortment optimization. We show that the assortment optimization problem under the Ising model is NP-hard. We also provide multiple theoretical insights into the structure of the optimal assortments based on the graphical representation of the Ising model, and propose several heuristic algorithms that can be used to obtain high-quality solutions to the assortment optimization problem. Our numerical analysis demonstrates that the developed simulated annealing procedure leads to an expected profit gain of 15% compared to offering an unoptimized assortment (where all products are included) and around 5% compared to using a revenue-ordered heuristic algorithm.

math.OC↗