The DOI matches its Crossref record.
Object-oriented software for quadratic programming
Abstract
The object-oriented software package OOQP for solving convex quadratic programming problems (QP) is described. The primal-dual interior point algorithms supplied by OOQP are implemented in a way that is largely independent of the problem structure. Users may exploit problem structure by supplying linear algebra, problem data, and variable classes that are customized to their particular applications. The OOQP distribution contains default implementations that solve several important QP problem types, including general sparse and dense QPs, bound-constrained QPs, and QPs arising from support vector machines and Huber regression. The implementations supplied with the OOQP distribution are based on such well known linear algebra packages as MA27/57, LAPACK, and PETSc. OOQP demonstrates the usefulness of object-oriented design in optimization software development, and establishes standards that can be followed in the design of software packages for other classes of optimization problems. A number of the classes in OOQP may also be reusable directly in other codes.
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
Cited by
All you need to know about model predictive control for buildings
Drgoňa, J., Arroyo, J., Cupeiro Figueroa, I., Blum, D., Arendt, K., Kim, D., Ollé, E. P., Oravec, J., Wetter, M., Vrabie, D. L., et al. (2020) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2020.09.001 · Cited by 1,020
MSVM-RFE: extensions of SVM-RFE for multiclass gene selection on DNA microarray data
Zhou, X., & Tuck, D. P. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm036 · Cited by 256
MyAdChoices: Bringing Transparency and Control to Online Advertising
Parra-Arnau, J., Achara, J. P., & Castelluccia, C. (2016) · arXiv · doi:10.48550/arxiv.1602.02046 · Cited by 20
Metadata
- Authors
- Gertz, E. M., & Wright, S. J.
- Venue
- ACM Transactions on Mathematical Software
- Published
- 2003-03
- Volume
- 29
- Issue
- 1
- Pages
- 58-81
- ISSN
- 0098-3500 (ISSN-L)
- 1557-7295
- OpenAlex
- W2080514141
- Cited by
- 253
- References
- 0
- FWCI
- 9.97
- 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: Object-oriented software for quadratic programming. https://app.cochat.ai/papers/10.1145/641876.641880
- The paper
- Gertz, E. M., & Wright, S. J. (2003). Object-oriented software for quadratic programming. ACM Transactions on Mathematical Software. https://doi.org/10.1145/641876.641880
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.
