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ACM Transactions on Mathematical Software is a peer-reviewed journal published by ACM, covering Matrix Theory and Algorithms, Numerical Methods and Algorithms, Parallel Computing and Optimization Techniques. Over its lifetime it has published 1,908 papers, which have received 141,082 citations.
| Publisher | ACM |
|---|---|
| Reference style | Numbered (ACM) Numbered — [1], [2] in the text [1] Ada Smith, Ben Jones, and Cara Lee. 2023. A representative article title. ACM Transactions on Mathematical Software 12, 3, Article 45 (2023).
Formats any DOI in ACM Transactions on Mathematical Software style. No sign-up. |
| Publishes research in | Matrix Theory and Algorithms Numerical Methods and Algorithms Parallel Computing and Optimization Techniques Advanced Optimization Algorithms Research Numerical methods for differential equations |
| ISSN | 0098-3500 |
| Citation impact (2-yr) | 1.3 |
| h-index | 161 |
| i10-index | 1,188 |
| Top institutions publishing here | Argonne National Laboratory |
| Journal website | toms.acm.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 convex hull of a set of points is the smallest convex set that contains the points. This article presents a practical convex hull algorithm that combines the two-dimensional Quickhull algorithm with the general-dimension Beneath-Beyond Algorithm. It is similar to the randomized, incremental algorithms for convex hull and delaunay triangulation. We provide empirical evidence that…
An iterative method is given for solving Ax ~ffi b and minU Ax -b 112, where the matrix A is large and sparse. The method is based on the bidiagonalization procedure of Golub and Kahan. It is analytically equivalent to the standard method of conjugate gradients, but possesses more favorable numerical properties.
We describe the University of Florida Sparse Matrix Collection, a large and actively growing set of sparse matrices that arise in real applications. The Collection is widely used by the numerical linear algebra community for the development and performance evaluation of sparse matrix algorithms. It allows for robust and repeatable experiments: robust because performance results…
L-BFGS-B is a limited-memory algorithm for solving large nonlinear optimization problems subject to simple bounds on the variables. It is intended for problems in which information on the Hessian matrix is difficult to obtain, or for large dense problems. L-BFGS-B can also be used for unconstrained problems and in this case performs similarly to its…
An algorithm and data structure are presented for searching a file containing N records, each described by k real valued keys, for the m closest matches or nearest neighbors to a given query record. The computation required to organize the file is proportional to kNlogN. The expected number of records examined in each search is…
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