The DOI matches its Crossref record.
The method of moving asymptotes—a new method for structural optimization
Abstract
A new method for non‐linear programming in general and structural optimization in particular is presented. In each step of the iterative process, a strictly convex approximating subproblem is generated and solved. The generation of these subproblems is controlled by so called ‘moving asymptotes’, which may both stabilize and speed up the convergence of the general process.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
No references could be resolved yet.
Research works printed but not linked: 4
- Schmit L. A., 1974, Some approximation concepts for structural synthesis, A I A A Journal, 12, 692
- 10.1007/978-94-009-8603-9_30 · doi:10.1007/978-94-009-8603-9_30
- 10.1002/nme.1620141203 · doi:10.1002/nme.1620141203
- 10.1007/BFb0121230 · doi:10.1007/bfb0121230
Cited by
Topology optimization approaches: A comparative review
Sigmund, O., & Maute, K. (2013) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-013-0978-6 · Cited by 2,890
Optimization methods applied to renewable and sustainable energy: A review
Baños, R., Manzano-Agugliaro, F., Montoya, F. G., Gil, C., Alcayde, A., & Gómez, J. (2011) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2010.12.008 · Cited by 1,646
Topology Optimization in Aircraft and Aerospace Structures Design
Zhu, J.-H., Zhang, W.-H., & Xia, L. (2016) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-015-9151-2 · Cited by 980
Topology optimization for nano‐photonics
Jensen, J. S., & Sigmund, O. (2011) · Laser & Photonics Reviews · doi:10.1002/lpor.201000014 · Cited by 767
Variations and extension of the convex–concave procedure
Lipp, T., & Boyd, S. (2016) · Optimization and Engineering · doi:10.1007/s11081-015-9294-x · Cited by 744
Metadata
- Authors
- Svanberg, K.
- Venue
- International Journal for Numerical Methods in Engineering
- Publisher
- Wiley
- Published
- 1987-02
- Volume
- 24
- Issue
- 2
- Pages
- 359-373
- ISSN
- 0029-5981 (ISSN-L)
- 1097-0207
- OpenAlex
- W2062523101
- Cited by
- 5,573
- References
- 0
- FWCI
- 69.12
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: The method of moving asymptotes—a new method for structural optimization. https://app.cochat.ai/papers/10.1002/nme.1620240207
- The paper
- Svanberg, K. (1987). The method of moving asymptotes—a new method for structural optimization. International Journal for Numerical Methods in Engineering. https://doi.org/10.1002/nme.1620240207
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
