An Efficient Optimal Algorithm for the Successive Minima Problem

TL;DR

This paper introduces a novel, efficient algorithm for solving the successive minima problem on lattices, significantly reducing computational complexity and outperforming existing methods in speed and memory usage.

Contribution

The paper presents a new optimal SMP algorithm with $igO(n^2)$ complexity, using a single search strategy and proven to be faster and more memory-efficient than prior algorithms.

Findings

Algorithm achieves $igO(n^2)$ memory complexity.

Proven to be $igO(n)$ times faster than existing algorithms.

Numerical simulations confirm efficiency and optimality.

Abstract

In many applications including integer-forcing linear multiple-input and multiple-output (MIMO) receiver design, one needs to solve a successive minima problem (SMP) on an $n$-dimensional lattice to get an optimal integer coefficient matrix $\A^\star\in \mathbb{Z}^{n\times n}$. In this paper, we first propose an efficient optimal SMP algorithm with an $\bigO(n^2)$ memory complexity. The main idea behind the new algorithm is it first initializes with a suitable suboptimal solution, which is then updated, via a novel algorithm with only $\bigO(n^2)$ flops in each updating, until $\A^{\star}$ is obtained. Different from existing algorithms which find $\A^\star$ column by column through using a sphere decoding search strategy $n$ times, the new algorithm uses a search strategy once only. We then rigorously prove the optimality of the proposed algorithm. Furthermore, we theoretically analyze its complexity. In particular, we not only show that the new algorithm is $\Omega(n)$ times faster than the most efficient existing algorithm with polynomial memory complexity, but also assert that it is even more efficient than the most efficient existing algorithm with exponential memory complexity. Finally, numerical simulations are presented to illustrate the optimality and efficiency of our novel SMP algorithm.