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Acta Numerica is a peer-reviewed journal published by Cambridge University Press, covering Advanced Numerical Methods in Computational Mathematics, Diverse Scientific and Economic Studies, Matrix Theory and Algorithms.
| Publisher | Cambridge University Press |
|---|---|
| Reference style | Numbered Numbered — [1], [2] in the text [1] A. Smith, B. Jones, and C. Lee, A representative article title, Acta Numerica 12 (2023) 45–58.
Formats any DOI in the closest standard style — Acta Numerica has no published style definition, so this is an approximation. No sign-up. |
| Publishes research in | Advanced Numerical Methods in Computational Mathematics Diverse Scientific and Economic Studies Matrix Theory and Algorithms Numerical methods for differential equations Legal case studies and regulations |
| ISSN | 0962-4929 |
| Citation impact (2-yr) | 16.05 |
| h-index | 111 |
| i10-index | 201 |
| Total citations | 56,992 |
| Top institutions publishing here | The University of Texas at Austin |
| Journal website | journals.cambridge.org |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
This paper is concerned with the mathematical structure of the immersed boundary (IB) method, which is intended for the computer simulation of fluid–structure interaction, especially in biological fluid dynamics. The IB formulation of such problems, derived here from the principle of least action, involves both Eulerian and Lagrangian variables, linked by the Dirac delta function.…
Large linear systems of saddle point type arise in a wide variety of applications throughout computational science and engineering. Due to their indefiniteness and often poor spectral properties, such linear systems represent a significant challenge for solver developers. In recent years there has been a surge of interest in saddle point problems, and numerous solution…
The subject of inverse problems in differential equations is of enormous practical importance, and has also generated substantial mathematical and computational innovation. Typically some form of regularization is required to ameliorate ill-posed behaviour. In this article we review the Bayesian approach to regularization, developing a function space viewpoint on the subject. This approach allows for…
Since its popularization in the late 1970s, Sequential Quadratic Programming (SQP) has arguably become the most successful method for solving nonlinearly constrained optimization problems. As with most optimization methods, SQP is not a single algorithm, but rather a conceptual method from which numerous specific algorithms have evolved. Backed by a solid theoretical and computational foundation,…
Monte Carlo is one of the most versatile and widely used numerical methods. Its convergence rate, O ( N −1/2 ), is independent of dimension, which shows Monte Carlo to be very robust but also slow. This article presents an introduction to Monte Carlo methods for integration problems, including convergence theory, sampling methods and variance…
