Starting CLuP with polytope relaxation

TL;DR

This paper introduces an improved version of the CLuP algorithm, called CLuP-plt, which uses polytope relaxation as a starting point, leading to faster convergence and better performance in polynomial time, especially in MIMO detection.

Contribution

It proposes a simple upgrade to the CLuP mechanism by incorporating polytope relaxation at initialization, enhancing efficiency and effectiveness in solving MIMO detection problems.

Findings

CLuP-plt outperforms the basic CLuP in complexity and accuracy.

In many cases, CLuP-plt achieves excellent results within three iterations.

Numerical experiments confirm the theoretical advantages of CLuP-plt.

Abstract

The Controlled Loosening-up (CLuP) mechanism that we recently introduced in \cite{Stojnicclupint19} is a generic concept that can be utilized to solve a large class of problems in polynomial time. Since it relies in its core on an iterative procedure, the key to its excellent performance lies in a typically very small number of iterations needed to execute the entire algorithm. In a separate paper \cite{Stojnicclupcmpl19}, we presented a detailed complexity analysis that indeed confirms the relatively small number of iterations. Since both papers, \cite{Stojnicclupint19} and \cite{Stojnicclupcmpl19} are the introductory papers on the topic we made sure to limit the initial discussion just to the core of the algorithm and consequently focused only on the algorithm's most basic version. On numerous occasions though, we emphasized that various improvements and further upgrades are possible. In this paper we present a first step in this direction and discuss a very simple upgrade that can be introduced on top of the basic CLuP mechanism. It relates to the starting of the CLuP and suggests the well-known so-called polytope-relaxation heuristic (see, e.g. \cite{StojnicBBSD05,StojnicBBSD08}) as the starting point. We refer to this variant of CLuP as the CLuP-plt and proceed with the presentation of its complexity analysis. As in \cite{Stojnicclupcmpl19}, a particular \textbf{\emph{complexity analysis per iteration level}} type of complexity analysis is chosen and presented through the algorithm's application on the well-known MIMO ML detection problem. As expected, the analysis confirms that CLuP-plt performs even better than the original CLuP. In some of the most interesting regimes it often achieves within the \textbf{\emph{first three iterations}} an excellent performance. We also complement the theoretical findings with a solid set of numerical experiments.