Variational and Majorization Principles in Lattice Reduction

TL;DR

This paper introduces a variational and majorization framework for lattice reduction, revealing new insights into swap mechanisms and proposing adaptive heuristics with improved performance.

Contribution

It develops a novel variational interpretation of lattice reduction swaps and introduces adaptive heuristics inspired by Schur-convex scoring rules.

Findings

Worst-case GSA envelope has a variational interpretation.

Thermal-Adaptive reduces operation counts on flat profiles.

Geodesic Deep-LLL reduces swap counts on structured lattices.

Abstract

Lattice reduction smooths the Gram-Schmidt profile, and we use majorization to describe the local swap mechanism behind that smoothing. In this language, each non-degenerate Lov\'asz swap acts as a T-transform on the log-norm profile. As a consequence, every strictly Schur-convex measure of profile spread decreases at such a swap. Two structural consequences follow. First, the worst-case GSA envelope admits a variational interpretation. It is the unique minimum-variance profile compatible with the Lov\'asz gap geometry, so its slope is determined by the LLL parameter alone. Second, the realized swap trajectory satisfies an exact telescoping identity for variance dissipation. The same viewpoint also helps organize deep-insertion heuristics. It suggests a thermal family of Schur-convex scoring rules, motivates adaptive selection within that family, and leads to two concrete selectors: Thermal-Adaptive, which reduces operation counts relative to SS-GG on flat profiles in our benchmarks while recovering SS-GG on $q$-ary inputs, and Geodesic Deep-LLL, which reduces equivalent-swap counts on structured lattices in our benchmarks at higher wall-clock cost.