APPLIED MATHEMATICS AND OPTIMIZATION

Scope & Guideline

Bridging Theory and Practice in Mathematical Sciences

Introduction

Delve into the academic richness of APPLIED MATHEMATICS AND OPTIMIZATION with our guidelines, detailing its aims and scope. Our resource identifies emerging and trending topics paving the way for new academic progress. We also provide insights into declining or waning topics, helping you stay informed about changing research landscapes. Evaluate highly cited topics and recent publications within these guidelines to align your work with influential scholarly trends.
LanguageMulti-Language
ISSN0095-4616
PublisherSPRINGER
Support Open AccessNo
CountryUnited States
TypeJournal
Convergefrom 1974 to 1977, from 1979 to 2024
AbbreviationAPPL MATH OPT / Appl. Math. Optim.
Frequency6 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressONE NEW YORK PLAZA, SUITE 4600 , NEW YORK, NY 10004, UNITED STATES

Aims and Scopes

The journal 'Applied Mathematics and Optimization' focuses on the application of mathematical theories and techniques to solve complex optimization problems across various scientific fields. It emphasizes both theoretical advancements and practical applications of optimization methods.
  1. Stochastic Control and Decision Processes:
    Research in this area encompasses the study of stochastic systems and decision-making processes under uncertainty, employing methods such as dynamic programming and stochastic differential equations.
  2. Optimal Control Theory:
    This includes the formulation and analysis of optimal control problems, particularly in systems governed by partial differential equations, focusing on finding control strategies that optimize a specific performance criterion.
  3. Numerical Methods and Algorithms:
    The journal features advancements in numerical techniques for solving mathematical models, including optimization algorithms, finite element methods, and simulations for complex systems.
  4. Mathematical Modeling in Various Applications:
    Papers often present mathematical models that describe real-world phenomena in fields such as biology, finance, and engineering, demonstrating the applicability of mathematical theories.
  5. Analysis of Partial Differential Equations (PDEs):
    Research involves the study of existence, uniqueness, and stability of solutions to various classes of PDEs, often in the context of control and optimization problems.
  6. Variational and Hemivariational Inequalities:
    This scope includes studies on inequalities that arise in optimization and control problems, particularly those involving non-smooth analysis and applications in mechanics.
The journal 'Applied Mathematics and Optimization' has seen a rise in specific research themes that reflect current trends in applied mathematics and optimization. These emerging scopes indicate the journal's responsiveness to evolving scientific challenges and interdisciplinary applications.
  1. Machine Learning and Data-Driven Optimization:
    Recent papers highlight the integration of machine learning techniques into optimization processes, indicating a trend towards data-driven approaches that enhance predictive modeling and decision-making.
  2. Mean Field Games and Control:
    There is a growing focus on mean field games, which study large populations of agents interacting with each other, reflecting an increasing interest in game-theoretic approaches to optimization.
  3. Nonlocal and Fractional Differential Equations:
    Research on nonlocal and fractional calculus has gained momentum, particularly in modeling complex systems in physics and biology, showcasing a broadening of mathematical tools and methodologies.
  4. Robust and Adaptive Control Systems:
    Emerging themes emphasize the development of robust optimization techniques that account for uncertainties and dynamic environments, essential for real-world applications.
  5. Computational Techniques for High-Dimensional Problems:
    As optimization problems become more complex, there is a noticeable trend towards developing computational methods that efficiently handle high-dimensional spaces, addressing significant challenges in optimization.

Declining or Waning

While 'Applied Mathematics and Optimization' continues to explore a broad range of topics, some areas have seen a decline in publication frequency or emphasis over recent years. The following themes appear to be waning in prominence.
  1. Classical Control Theory:
    Research focusing on traditional control methodologies has seen less emphasis, as the field shifts towards more complex and flexible approaches involving stochastic and adaptive systems.
  2. Linear Programming Techniques:
    With advancements in non-linear and stochastic optimization methods, traditional linear programming methods have become less central in recent publications.
  3. Single-Agent Decision Models:
    The focus on single-agent optimization problems has diminished in favor of multi-agent and game-theoretic approaches, reflecting a broader interest in collaborative and competitive decision-making scenarios.
  4. Static Optimization Problems:
    There is a noticeable shift from static to dynamic optimization problems, as researchers increasingly investigate systems that evolve over time and require adaptive strategies.

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