Foundations of Computational Mathematics

Aims & Scope

Foundations of Computational Mathematics (FoCM) will publish research and survey papers of the highest quality which further the understanding of the connections between mathematics and computation.

The journal aims to promote the exploration of all fundamental issues underlying the creative tension among mathematics, computer science and application areas unencumbered by any external criteria such as the pressure for applications.

The journal will thus serve an increasingly important and applicable area of mathematics.

The journal hopes to further the understanding of the deep relationships between mathematical theory: analysis, topology, geometry and algebra, and the computational processes as they are evolving in tandem with the modern computer.

With its distinguished editorial board selecting papers of the highest quality and interest from the international community, FoCM hopes to influence both mathematics and computation.

Relevance to applications will not constitute a requirement for the publication of articles.

The journal does not accept code for review however authors who have code/data related to the submission should include a weblink to the repository where the data/code is stored.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	2.7
2024	2.50

Journal Rank

Year	Value
2024	1101

Journal Citation Indicator

Year	Value
2024	502

SJR (SCImago Journal Rank)

Year	Value
2024	2.308

Quartile

Year	Value
2024	Q1

h-index

Year	Value
2024	71

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science and Mathematics, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Exact Matrix Completion via Convex Optimization

  Citation: 3323

  Authors: Emmanuel J., Benjamin

• The Convex Geometry of Linear Inverse Problems

  Citation: 749

  Authors: Venkat, Benjamin, Pablo A., Alan S.

• Uniform Uncertainty Principle and Signal Recovery via Regularized Orthogonal Matching Pursuit

  Citation: 680

  Authors: Deanna, Roman

• User-Friendly Tail Bounds for Sums of Random Matrices

  Citation: 581

  Authors: Joel A.

• Random Gradient-Free Minimization of Convex Functions

  Citation: 488

  Authors: Yurii, Vladimir

• Adaptive Restart for Accelerated Gradient Schemes

  Citation: 407

  Authors: Brendan, Emmanuel

• Quantitative Robust Uncertainty Principles and Optimally Sparse Decompositions

  Citation: 346

  Authors: Emmanuel J., Justin

• A Rigorous ODE Solver and Smale’s 14th Problem

  Citation: 324

  Authors: Warwick

• Optimality of a Standard Adaptive Finite Element Method

  Citation: 307

  Authors: Rob

• Trust-Region Methods on Riemannian Manifolds

  Citation: 302

  Authors: P.-A., C.G., K.A.

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