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SIAM Journal on Scientific Computing is a peer-reviewed journal published by SIAM, covering Advanced Numerical Methods in Computational Mathematics, Matrix Theory and Algorithms, Numerical methods for differential equations. Over its lifetime it has published 5,857 papers, which have received 287,176 citations.
| Publisher | SIAM |
|---|---|
| Reference style | Numbered (SIAM) Numbered — [1], [2] in the text [1] A. Smith, B. Jones, and C. Lee, A representative article title, SIAM Journal on Scientific Computing 12 (2023) 45–58.
Formats any DOI in the closest standard style — SIAM Journal on Scientific Computing has no published style definition, so this is an approximation. No sign-up. |
| Publishes research in | Advanced Numerical Methods in Computational Mathematics Matrix Theory and Algorithms Numerical methods for differential equations Computational Fluid Dynamics and Aerodynamics Electromagnetic Scattering and Analysis |
| ISSN | 1064-8275 |
| Citation impact (2-yr) | 3.04 |
| h-index | 210 |
| i10-index | 3,717 |
| Top institutions publishing here | Centre National de la Recherche Scientifique |
| Journal website | www.siam.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.
The time-frequency and time-scale communities have recently developed a large number of overcomplete waveform dictionaries --- stationary wavelets, wavelet packets, cosine packets, chirplets, and warplets, to name a few. Decomposition into overcomplete systems is not unique, and several methods for decomposition have been proposed, including the method of frames (MOF), Matching pursuit (MP), and, for…
An algorithm for solving large nonlinear optimization problems with simple bounds is described. It is based on the gradient projection method and uses a limited memory BFGS matrix to approximate the Hessian of the objective function. It is shown how to take advantage of the form of the limited memory approximation to implement the algorithm…
Recently, a number of researchers have investigated a class of graph partitioning algorithms that reduce the size of the graph by collapsing vertices and edges, partition the smaller graph, and then uncoarsen it to construct a partition for the original graph [4, 26]. From the early work it was clear that multilevel techniques held great…
Abstract. We present a new method for solving stochastic di®erential equations based on Galerkin projections and extensions of Wiener's polynomial chaos. Speci¯cally, we represent the stochastic processes with an optimum trial basis from the Askey family of orthogonal polynomials that reduces the dimensionality of the system and leads to exponential convergence of the error. Several…
This paper describes mathematical and software developments for a suite of programs for solving ordinary dierential equations in Matlab.
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