EDBT 2026 Demo / reviewers in the wild / expert
Weijiang Hu
dblp:435/9704
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2026
0009-0002-7585-2497ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Theory of computation · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Theoretical computer science
1 paper |
Mathematical optimization · 59% Algorithmic game theory and mechanism design · 41% |
Topics — the 7 heaviest of 7, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Mathematical optimization › continuous optimization
convex optimization |
1.0 | 1 | 2026 | New Convex Programming Technique for Nash Social Welfare and Scheduling · ICALP 2026 |
Mathematical optimization
convex relaxation |
1.0 | 1 | 2026 | New Convex Programming Technique for Nash Social Welfare and Scheduling · ICALP 2026 |
Algorithmic game theory and mechanism design
fair division |
1.0 | 1 | 2026 | New Convex Programming Technique for Nash Social Welfare and Scheduling · ICALP 2026 |
Algorithmic game theory and mechanism design › welfare maximization
nash social welfare |
1.0 | 1 | 2026 | New Convex Programming Technique for Nash Social Welfare and Scheduling · ICALP 2026 |
Mathematical optimization
combinatorial optimization |
0.3 | 1 | 2026 | New Convex Programming Technique for Nash Social Welfare and Scheduling · ICALP 2026 |
Mathematical optimization
scheduling |
0.3 | 1 | 2026 | New Convex Programming Technique for Nash Social Welfare and Scheduling · ICALP 2026 |
Mathematical optimization › scheduling › parallel machine scheduling
unrelated machines |
0.3 | 1 | 2026 | New Convex Programming Technique for Nash Social Welfare and Scheduling · ICALP 2026 |
Methods — techniques the papers use, named apart from their topics
fisher market · 1.0convex programming · 1.0LP rounding · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | New Convex Programming Technique for Nash Social Welfare and SchedulingabstractWe propose a new convex programming relaxation for the weighted Nash social welfare (NSW) problem that achieves a matching (e^{1/e} ≈ 1.445)-approximation via the rounding algorithm of Feng and Li. Unlike the exponential-size configuration LP used in prior work, our formulation can be converted into a compact linear program of polynomial size, incurring only an additive loss of ln(1+ε) in the objective. This allows the program to be solved directly using standard LP solvers, without the ellipsoid method or dual separation oracles. In the unweighted case, we show that our convex program is equivalent to the restricted-spending Fisher market convex program of Cole and Gkatzelis, yielding a constructive proof that its integrality gap is exactly e^{1/e}. With a minor modification, our analysis also gives a simple proof of the e^{1/e} EF1 gap for the identical agent setting. Finally, we show that our convex programming technique extends to two unrelated machine scheduling problems, recovering the best-known approximation ratios with simpler analyses. Yuda Feng, Weijiang Hu, Shi Li 0001 |
ICALP | 2 |