Dimension-Free Descriptions of Convex Sets

TL;DR

This paper introduces a framework based on representation stability for understanding and constructing dimension-free descriptions of convex sets, enabling scalable algorithms and unified solutions across dimensions.

Contribution

It develops a systematic approach to dimension-free convex set descriptions, linking algebraic topology with convex geometry, and provides practical algorithms and unified solution methods.

Findings

Dimension-free descriptions relate to representation stability in algebraic topology.

The framework allows construction of parametric families of convex sets for various applications.

Many symmetric conic programs can be solved in constant time using the proposed approach.

Abstract

Convex sets arising in a variety of applications are well-defined for every relevant dimension. Examples include the simplex and the spectraplex that correspond to probability distributions and to quantum states; combinatorial polytopes and their relaxations such as the cut polytope and the elliptope in integer programming; and unit balls of regularizers such as the $\ell_p$ and Schatten norms in inverse problems. Moreover, these sets are often specified using conic descriptions that can be obviously instantiated in any dimension. We develop a systematic framework to study such dimension-free descriptions of convex sets. We show that dimension-free descriptions arise from a recently-identified phenomenon in algebraic topology called representation stability, which relates invariants across dimensions in a sequence of group representations. Our framework yields structural results for dimension-free descriptions pertaining to the relations between the sets they describe across dimensions, extendability of a single set in a given dimension to a freely-described sequence, and continuous limits of such sequences. We also develop a procedure to obtain parametric families of freely-described convex sets whose structure is adapted to a given application; illustrations are provided via examples that arise in the literature as well as new families that are derived using our procedure. We demonstrate the utility of our framework in two contexts. First, we develop an algorithm for a dimension-free analog of the convex regression problem, where a convex function is fit to input-output data; by searching over our parametric families, we can fit a function to low-dimensional inputs and extend it to any other dimension. Second, we prove that many sequences of symmetric conic programs can be solved in constant time, which unifies and strengthens several results in the literature.