The DOI matches its Crossref record; 154 of the 154 references it cites were found in a registry or an index.
The Design of Approximation Algorithms
Abstract
Discrete optimization problems are everywhere, from traditional operations research planning (scheduling, facility location and network design); to computer science databases; to advertising issues in viral marketing. Yet most such problems are NP-hard; unless P = NP, there are no efficient algorithms to find optimal solutions. This book shows how to design approximation algorithms: efficient algorithms that find provably near-optimal solutions. The book is organized around central algorithmic techniques for designing approximation algorithms, including greedy and local search algorithms, dynamic programming, linear and semidefinite programming, and randomization. Each chapter in the first section is devoted to a single algorithmic technique applied to several different problems, with more sophisticated treatment in the second section. The book also covers methods for proving that optimization problems are hard to approximate. Designed as a textbook for graduate-level algorithm courses, it will also serve as a reference for researchers interested in the heuristic solution of discrete optimization problems.
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
(1979) · Cited by 43,841
A note on two problems in connexion with graphs
Dijkstra, E. W. (1959) · Numerische Mathematik · doi:10.1007/bf01386390 · Cited by 24,043
Horn, R. A., & Johnson, C. R. (1985) · Cited by 22,203
Adhi Harmoko, S., M.Komp, Jacquard, J. M., Zuse, K., & Eniac (2005) · Cited by 18,035
Fast approximate energy minimization via graph cuts
Boykov, Y., Veksler, O., & Zabih, R. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.969114 · Cited by 6,988
Probability Inequalities for sums of Bounded Random Variables
Hoeffding, W. (1994) · Springer Series in Statistics · doi:10.1007/978-1-4612-0865-5_26 · Cited by 6,666
Combinatorial Optimization: Algorithms and Complexity.
Johnson, D. S., Papadimitriou, C. H., & Steiglitz, K. (1984) · The American Mathematical Monthly · doi:10.2307/2322374 · Cited by 5,942
Integer and Combinatorial Optimization
Williams, H. P., Nemhauser, G. L., & Wolsey, L. A. (1990) · Journal of the Operational Research Society · doi:10.2307/2583737 · Cited by 5,485
Motwani, R., & Raghavan, P. (1995) · Cambridge University Press eBooks · doi:10.1017/cbo9780511814075 · Cited by 5,123
An analysis of approximations for maximizing submodular set functions—I
Nemhauser, G. L., Wolsey, L. A., & Fisher, M. L. (1978) · Mathematical Programming · doi:10.1007/bf01588971 · Cited by 4,412
Cited by
Benchmarking the quantum approximate optimization algorithm
Willsch, M., Willsch, D., Jin, F., De Raedt, H., & Michielsen, K. (2020) · Quantum Information Processing · doi:10.1007/s11128-020-02692-8 · Cited by 128
Low depth mechanisms for quantum optimization
McClean, J. R., Harrigan, M. P., Mohseni, M., Rubin, N. C., Jiang, Z., Boixo, S., Smelyanskiy, V. N., Babbush, R., & Neven, H. (2020) · arXiv · doi:10.48550/arxiv.2008.08615 · Cited by 45
Computing Education Research Today
Fincher, S. A., Tenenberg, J., Dorn, B., Hundhausen, C., McCartney, R., & Murphy, L. (2019) · The Cambridge Handbook of Computing Education Research · doi:10.1017/9781108654555.003 · Cited by 7
Metadata
- Authors
- Williamson, D. P., & Shmoys, D. B.
- Venue
- Cambridge University Press eBooks
- Publisher
- Cambridge University Press
- Published
- 2011-04-26
- OpenAlex
- W1899379166
- Cited by
- 1,301
- References
- 154
- FWCI
- 16.51
- Type
- book
- 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 Design of Approximation Algorithms. https://app.cochat.ai/papers/10.1017/cbo9780511921735
- The paper
- Williamson, D. P., & Shmoys, D. B. (2011). The Design of Approximation Algorithms. Cambridge University Press eBooks. https://doi.org/10.1017/cbo9780511921735
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.
