LINEAR & MULTILINEAR ALGEBRA

Scope & Guideline

Shaping the Future of Linear and Multilinear Algebra

Introduction

Immerse yourself in the scholarly insights of LINEAR & MULTILINEAR ALGEBRA with our comprehensive guidelines detailing its aims and scope. This page is your resource for understanding the journal's thematic priorities. Stay abreast of trending topics currently drawing significant attention and explore declining topics for a full picture of evolving interests. Our selection of highly cited topics and recent high-impact papers is curated within these guidelines to enhance your research impact.
LanguageEnglish
ISSN0308-1087
PublisherTAYLOR & FRANCIS LTD
Support Open AccessNo
CountryUnited Kingdom
TypeJournal
Convergefrom 1973 to 2024
AbbreviationLINEAR MULTILINEAR A / Linear Multilinear Algebra
Frequency12 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
Address2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND

Aims and Scopes

The journal 'Linear & Multilinear Algebra' focuses on advancing the theoretical understanding and practical applications of linear and multilinear algebra. It serves as a platform for disseminating original research that spans a diverse range of topics related to matrix theory, operator theory, and their intersections with various mathematical fields.
  1. Matrix Theory and Applications:
    The journal extensively covers topics related to matrices, including their properties, decompositions, inverses, and applications in various mathematical contexts such as numerical linear algebra and optimization.
  2. Operator Theory:
    Research on linear operators, including bounded and unbounded operators, their spectral properties, and applications in functional analysis, is a significant focus area.
  3. Graph Theory and Matrices:
    Papers exploring the relationships between graph theory and matrix theory, including spectral graph theory, Laplacian matrices, and applications of matrix properties to graph structures, are frequently published.
  4. Multilinear Algebra:
    The exploration of multilinear mappings, tensor products, and their applications is a vital area of research, addressing complex structures beyond traditional linear algebra.
  5. Numerical Methods and Algorithms:
    Research on computational techniques, numerical stability, and algorithm development for solving matrix equations and linear systems is a prominent theme.
  6. Quantum Computing and Information Theory:
    The journal includes studies on matrices and operators in the context of quantum mechanics, focusing on their role in quantum computing and information theory.
The journal 'Linear & Multilinear Algebra' is witnessing a rise in various innovative themes and emerging topics that reflect the evolving landscape of research in linear and multilinear algebra.
  1. Applications in Machine Learning and Data Science:
    There is a noticeable increase in papers exploring the application of linear and multilinear algebra concepts in machine learning, data analysis, and related computational fields.
  2. Quantum Algebra and Information Theory:
    Research focusing on the role of linear and multilinear algebra in quantum computing and information theory is on the rise, highlighting the intersection of algebra with quantum mechanics.
  3. Advanced Numerical Methods:
    Papers discussing sophisticated numerical methods for matrix computations, including iterative algorithms and optimization techniques, are increasingly common.
  4. Graph Theory Applications:
    The integration of graph theory with linear algebra, particularly in spectral graph theory and network analysis, is gaining traction, reflecting an interdisciplinary approach.
  5. Tensor Analysis and Multilinear Maps:
    There is growing interest in the study of tensors, multilinear maps, and their applications across various mathematical and applied contexts, indicating a shift towards more complex structures.

Declining or Waning

While 'Linear & Multilinear Algebra' continues to thrive in many areas, some themes appear to be declining in prominence, reflecting shifts in research focus and emerging trends.
  1. Classical Matrix Theory:
    Traditional topics in matrix theory, such as basic properties and simple applications, are becoming less prevalent as the field advances toward more complex and applied aspects.
  2. Elementary Linear Algebra:
    The publication of papers addressing fundamental concepts of linear algebra, such as basic vector spaces and simple matrix operations, has decreased, indicating a shift towards advanced applications.
  3. Static Applications of Linear Algebra:
    Research that solely focuses on static applications of linear algebra without integrating modern computational approaches or interdisciplinary applications seems to be waning.

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