{"title":"New Results on the Remote Set Problem and Its Applications in Complexity Study","authors":"Yijie Chen, Kewei Lv","doi":"10.1007/s00224-024-10162-2","DOIUrl":null,"url":null,"abstract":"<p>In 2015, Haviv introduced the Remote set problem (RSP) and studied the complexity of the covering radius problem (CRP), which is a classical problem in lattices. The RSP aims to identify a set containing a point that is sufficiently distant from a given lattice <span>\\(\\pmb {\\mathcal {L}}\\)</span>. It introduced a new method for analyzing the complexity of CRP. An open question in RSP is whether we can obtain the approximation factor <span>\\(\\gamma =1/2\\)</span>. This paper investigates this question and proposes a probabilistic polynomial-time algorithm for RSP with an approximation factor of <span>\\(1/2-1/(c\\lambda ^{(p)}_n)\\)</span>, where <span>\\(c\\in \\mathbb {Z}^{+}\\)</span> and <span>\\(\\lambda ^{(p)}_n\\)</span> is the <i>n</i>-th successive minima in lattice under <span>\\(l_p\\)</span>-norm. For a given lattice <span>\\(\\pmb {\\mathcal {L}}\\)</span> with rank <i>n</i> and positive integer <i>d</i>, our algorithm outputs a set <i>S</i> of size <i>d</i> in polynomial time. This set <i>S</i> includes a point at least <span>\\((\\frac{1}{2}-\\frac{1}{c\\lambda ^{(p)}_n}){{\\rho }^{(p)}}(\\pmb {\\mathcal {L}})\\)</span> from lattice <span>\\(\\pmb {\\mathcal {L}}\\)</span> with a probability greater than <span>\\(1-1/2^d\\)</span>. Here, <i>c</i> is a positive integer and <span>\\(\\rho ^{(p)}(\\pmb {\\mathcal {L}})\\)</span> denotes the covering radius of <span>\\(\\pmb {\\mathcal {L}}\\)</span> in <span>\\(l_p\\)</span>-norm(<span>\\(1\\le p\\le \\infty \\)</span>). Based on this, we obtain that <span>\\(\\text {GAPCRP}_{2+1/2^{O(n)}}\\)</span> belongs to the complexity class coRP, and we provide new reductions from GAPCRP to GAPCVP.</p>","PeriodicalId":22832,"journal":{"name":"Theory of Computing Systems","volume":null,"pages":null},"PeriodicalIF":0.6000,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Theory of Computing Systems","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s00224-024-10162-2","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
In 2015, Haviv introduced the Remote set problem (RSP) and studied the complexity of the covering radius problem (CRP), which is a classical problem in lattices. The RSP aims to identify a set containing a point that is sufficiently distant from a given lattice \(\pmb {\mathcal {L}}\). It introduced a new method for analyzing the complexity of CRP. An open question in RSP is whether we can obtain the approximation factor \(\gamma =1/2\). This paper investigates this question and proposes a probabilistic polynomial-time algorithm for RSP with an approximation factor of \(1/2-1/(c\lambda ^{(p)}_n)\), where \(c\in \mathbb {Z}^{+}\) and \(\lambda ^{(p)}_n\) is the n-th successive minima in lattice under \(l_p\)-norm. For a given lattice \(\pmb {\mathcal {L}}\) with rank n and positive integer d, our algorithm outputs a set S of size d in polynomial time. This set S includes a point at least \((\frac{1}{2}-\frac{1}{c\lambda ^{(p)}_n}){{\rho }^{(p)}}(\pmb {\mathcal {L}})\) from lattice \(\pmb {\mathcal {L}}\) with a probability greater than \(1-1/2^d\). Here, c is a positive integer and \(\rho ^{(p)}(\pmb {\mathcal {L}})\) denotes the covering radius of \(\pmb {\mathcal {L}}\) in \(l_p\)-norm(\(1\le p\le \infty \)). Based on this, we obtain that \(\text {GAPCRP}_{2+1/2^{O(n)}}\) belongs to the complexity class coRP, and we provide new reductions from GAPCRP to GAPCVP.
期刊介绍:
TOCS is devoted to publishing original research from all areas of theoretical computer science, ranging from foundational areas such as computational complexity, to fundamental areas such as algorithms and data structures, to focused areas such as parallel and distributed algorithms and architectures.