Weijiang Hu

dblp:435/9704 · DBLP profile ↗
← Back
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

TopicWeightPapersLastEvidence papers
Mathematical optimization › continuous optimization
convex optimization
1.012026
New Convex Programming Technique for Nash Social Welfare and Scheduling · ICALP 2026
Mathematical optimization
convex relaxation
1.012026
New Convex Programming Technique for Nash Social Welfare and Scheduling · ICALP 2026
Algorithmic game theory and mechanism design
fair division
1.012026
New Convex Programming Technique for Nash Social Welfare and Scheduling · ICALP 2026
Algorithmic game theory and mechanism design › welfare maximization
nash social welfare
1.012026
New Convex Programming Technique for Nash Social Welfare and Scheduling · ICALP 2026
Mathematical optimization
combinatorial optimization
0.312026
New Convex Programming Technique for Nash Social Welfare and Scheduling · ICALP 2026
Mathematical optimization
scheduling
0.312026
New Convex Programming Technique for Nash Social Welfare and Scheduling · ICALP 2026
Mathematical optimization › scheduling › parallel machine scheduling
unrelated machines
0.312026
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
YearPublicationVenuePosition
2026 New Convex Programming Technique for Nash Social Welfare and Scheduling
abstract
We 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
ICALP2