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Higher Dimensional Homological Algebra

Higher Dimensional Homological Algebra
高维同调代数
批准号:
EP/P016014/1
负责人:
Peter Jorgensen
金额:
$30.73万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2017
资助国家:
英国
项目状态:
已结题
起止时间:
2017 至 --

项目摘要

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中文摘要
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英文摘要
Classic homological algebra had its origin some 60 years ago in algebraic topology. Since then, it has grown to a theory with applications to many areas of mathematics, including combinatorics, geometry, and representation theory. Classic homological algebra is often phrased as the theory of abelian and triangulated categories.Higher dimensional homological algebra is a new development of the last decade. It is the theory of n-abelian categories and (n+2)-angulated categories, where n is a positive integer. In these categories, the role previously played by 1-extensions is taken over by n-extensions. Note that the case n=1 gives ordinary abelian and triangulated categories, hence classic homological algebra. We refer to (n+2)-angulated categories because the case n=1 gives triangulated categories. Higher dimensional homological algebra is currently very active. It has applications to algebraic geometry, combinatorics, and the representation theory of finite dimensional algebras. There are substantial contributions from a number of strong mathematicians (Iyama, Keller, Reiten). Some of the combinatorial structures which appear, like higher dimensional cyclic polytopes, are novel to homological algebra and representation theory.This project will provide three key items currently missing in higher dimensional homological algebra: Tilting objects, higher dimensional derived categories, and higher dimensional model categories. We will define tilting objects in higher dimensional homological algebra and show a version of the Ingalls-Thomas bijections between support tilting objects, torsion classes, intermediate t-structures, and non-crossing partitions. This will enhance and illuminate the links to combinatorics. We will define higher dimensional derived categories and higher dimensional model categories. This will provide the right context for higher dimensional tilting theory, and give a comprehensive framework for higher dimensional homological algebra.
期刊论文(9)
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会议论文
Friezes, weak friezes, and T-paths
饰带、弱饰带和 T 形路径
DOI: 10.48550/arxiv.2005.06230
发表时间: 2020
期刊:
影响因子: --
作者: [Canakci I]
通讯作者: Canakci I
Infinite friezes and triangulations of annuli
无限的饰带和环带的三角剖分
DOI: 10.1142/s0219498824502074
发表时间: 2023
期刊: Journal of Algebra and Its Applications
影响因子: 0.8
作者: [Baur K]
通讯作者: Baur K
Lattice bijections for string modules, snake graphs and the weak Bruhat order
字符串模块、蛇图和弱 Bruhat 阶的格双射
DOI: 10.48550/arxiv.1811.06064
发表时间: 2018
期刊:
影响因子: --
作者: [Canakci I]
通讯作者: Canakci I
Addendum and Erratum: Mapping cones for morphisms involving a band complex in the bounded derived category of a gentle algebra
附录和勘误:涉及温和代数有界派生范畴中带复形的态射的映射锥
DOI: 10.48550/arxiv.2001.06435
发表时间: 2020
期刊:
影响因子: --
作者: [Canakci I]
通讯作者: Canakci I
6
    Workshop on Triangulations and Mutations
    • 批准号:
      EP/K003720/1
    • 项目类别:
      Research Grant
    • 资助金额:
      $2.03万
    • 财政年份:
      2012
    • 负责人:
      Peter Jorgensen
    • 依托单位:
    Understanding the Spatial Patterns of Diversity of Montane Forests in Northern Bolivia
    • 批准号:
      0743457
    • 项目类别:
      Continuing Grant
    • 资助金额:
      $45.0万
    • 财政年份:
      2008
    • 负责人:
      Peter Jorgensen
    • 依托单位:
    REVSYS Collaborative Research: Untangling the Passionflower Vines: Phylogeny, Species Diversification, and Character Evolution in Passiflora Ssubg. Decaloba (Passifloraceae)
    • 批准号:
      0717115
    • 项目类别:
      Continuing Grant
    • 资助金额:
      $28.44万
    • 财政年份:
      2007
    • 负责人:
      Peter Jorgensen
    • 依托单位:
    Botanical Inventory of the Madidi Region, Bolivia
    • 批准号:
      0101775
    • 项目类别:
      Standard Grant
    • 资助金额:
      $22.5万
    • 财政年份:
      2001
    • 负责人:
      Peter Jorgensen
    • 依托单位:
    国内基金
    海外基金
    Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis