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An analysis of approximations for maximizing submodular set functions—II
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When per-pool KV capacity is slack, the partition matroid M1 is the only binding constraint and the combinatorial (lazy) greedy deployed on the fast path returns A_g with G(A_g) ≥ 1/2 G*.
“When per-pool KV capacity is slack (the common case), M 1 is the only binding constraint and the combinatorial (lazy) greedy deployed on the fast path returns A g with G ( A g ) ≥ 1 2 G ⋆ [54]”
From Janus: Joint Prefill/Decode Disaggregation with KV-Cache-Aware Multi-Cloud Routing for Edge-Adjacent LLM Serving (2026) · cites this paper as [54]
Submodular maximization (from the classical greedy analysis of Nemhauser-Wolsey-Fisher [12]) underpins cache and content placement [20]; the matroid-constrained guarantees we invoke are due to Fisher-Nemhauser-Wolsey [54] (combinatorial greedy, 1/2), Calinescu-Chekuri-Pál-Vondrák [53] (continuous greedy, 1-1/e), and, for the matroid-plus-knapsack case, Lee-Mirrokni-Nagarajan-Sviridenko [62] (local
“Submodular maximization (from the classical greedy analysis of Nemhauser-Wolsey-Fisher [12]) underpins cache and content placement [20]; the matroid-constrained guarantees we invoke are due to Fisher-Nemhauser-Wolsey [54] (combinatorial greedy, 1 2 ), Calinescu-Chekuri-Pál-Vondrák [53] (continuous greedy, 1 -1 /e ), and, for the matroid-plus-knapsack case, Lee-Mirrokni-Nagarajan-Sviridenko [62] (”
From Janus: Joint Prefill/Decode Disaggregation with KV-Cache-Aware Multi-Cloud Routing for Edge-Adjacent LLM Serving (2026) · cites this paper as [54]
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Cites
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
Self-citationP-Complete Approximation Problems
Sahni, S. K., & Gonzalez, T. F. (1976) · Journal of the ACM · doi:10.1145/321958.321975 · Cited by 1,725
Cornuéjols, G., Fisher, M. L., & Nemhauser, G. L. (1977) · Management Science · doi:10.1287/mnsc.23.8.789 · Cited by 793
Self-citationLectures on Applied Mathematics
Bungartz, H., Hoppe, R. H. W., & Zenger, C. (2000) · Springer eBooks · doi:10.1007/978-3-642-59709-1 · Cited by 772
Submodular Functions, Matroids, and Certain Polyhedra
Edmonds, J. (2003) · Lecture notes in computer science · doi:10.1007/3-540-36478-1_2 · Cited by 543
An Analysis of the Greedy Heuristic for Independence Systems
Körte, B., & Hausmann, D. (1978) · Annals of discrete mathematics · doi:10.1016/s0167-5060(08)70322-4 · Cited by 192
Worst-Case Analysis of a Placement Algorithm Related to Storage Allocation
Chandra, A. K., & Wong, C. K. (1975) · SIAM Journal on Computing · doi:10.1137/0204021 · Cited by 126
Introduction to the Theory of Matroids
Randow, R. (1975) · Lecture notes in economics and mathematical systems · doi:10.1007/978-3-642-48292-2 · Cited by 37
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- J. Edmonds, “Matroid partition”, in: G.B. Dantzig and A.M. Veinott, eds., Mathematics of the decision sciences, Lectures in Applied Mathematics, 11, (Am. Math. Soc. Providence, RI, 1968) pp. 333–345.
- T.A. Jenkyns, “The efficiency of the “greedy” algorithm”, in: Proceedings of the 7th S.E. Conference on combinatorics, graph theory and computing (Utilitas Mathematica, Winnipeg, 1976) pp. 341–350.
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Metadata
- Authors
- Fisher, M. L., Nemhauser, G. L., & Wolsey, L. A.
- Affiliations
- University of Pennsylvania (US)
- UCLouvain (BE)
- Venue
- Mathematical Programming Studies
- Publisher
- Springer Berlin Heidelberg
- Published
- 1978
- Pages
- 73-87
- ISSN
- 0303-3929 (ISSN-L)
- OpenAlex
- W2757107770
- Cited by
- 589
- References
- 9
- Citations by year
- 2026: 15 · 2025: 28 · 2024: 30 · 2023: 45 · 2022: 57 · 2021: 35 · 2020: 53 · 2019: 40 · 2018: 37 · 2017: 35 · 2016: 27 · 2015: 35 · 2014: 20 · 2013: 29 · 2012: 18
- FWCI
- 7.98
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Mathematical programming studies (publishedVersion)
- Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Complexity and Algorithms in Graphs
- Physical Sciences › Mathematics › Discrete Mathematics and Combinatorics › Advanced Combinatorial Mathematics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Game Theory and Voting Systems
- Keywords
- submodular functions, maximization, approximation algorithms, greedy algorithm, linear programming relaxation, matroid intersection, worst-case bounds, nondecreasing functions
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 ›
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