Optimization Algorithms in Data Science: Methods and Theory

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Computational and Applied Mathematics".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 226

Special Issue Editor

Data Science and AI division, Department of Computer Science and Engineering, Chalmers University of Technologydisabled, SE-412 96 Gothenburg, Sweden
Interests: optimization algorithms; data science

Special Issue Information

Dear Colleagues,

Optimization algorithms lie at the heart of many chief technologies concerning data, for at least two main reasons. On one hand, they serve as one the most versatile computational toolsets of data science. On the other hand, they provide a principled methodology for formulating data-related problems. Many procedures for data processing and inference, especially statistical approaches, rely on mathematical optimization frameworks, the design of which heavily depends on the structure of the data and the underlying tasks. The purpose of this Special Issue it to address recent advances within this area, in various respects, including formulating various applied tasks as optimization problems, designing algorithmic strategies for solving optimization problems, and analyzing optimization algorithmic solutions in data science.

We consider a wide range of contributions, from proposing new algorithmic solutions to various applied questions using optimization tools, to theoretical studies, e.g., about the convergence of optimization algorithms or statistical analysis of the optimal solutions. We are not restricted to certain types of application. The topics of interest include, but are not limited to, the following:

Algorithms for:

  • Convex optimization;
  • Nonconvex optimization;
  • Discrete optimization;
  • Linear and Convex relaxation;
  • Large-scale optimization;
  • Distributed optimization;
  • Stochastic optimization. 

Optimization applied to:

  • Machine learning;
  • Computer vision;
  • Image processing;
  • Signal processing;
  • Natural Language Processing;
  • Neural Networks;
  • Knowledge transfer;
  • Data fusion;
  • Feature extraction. 

Analysis:

  • Relaxation guarantees;
  • Convergence analysis of optimization algorithms;
  • Hardness of optimization problems;
  • Implicit regularization of optimization algorithms.

Dr. Ashkan Panahi
Guest Editor

Manuscript Submission Information

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Published Papers

This special issue is now open for submission.
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