Demo Data from SIGIR 2026 / reviewers in the wild / expert
Wen-Lin Hsieh
dblp:73/3823
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 1995
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1
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.
| Computer networks
1 paper |
Routing and switching · 100% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Parallel and multicore computing · 100% | |
| Theoretical computer science
1 paper |
Mathematical optimization · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Routing and switching › routing algorithms
optimal routing |
0.0 | 1 | 1995 | Exact Convergence of a Parallel Textured Algorithm for Data Network Optimal Routing Problems · IEEE Trans. Parallel Distributed Syst. 1995 |
Parallel and multicore computing
parallel algorithms |
0.0 | 1 | 1995 | Exact Convergence of a Parallel Textured Algorithm for Data Network Optimal Routing Problems · IEEE Trans. Parallel Distributed Syst. 1995 |
Mathematical optimization
distributed optimization |
0.0 | 1 | 1995 | Exact Convergence of a Parallel Textured Algorithm for Data Network Optimal Routing Problems · IEEE Trans. Parallel Distributed Syst. 1995 |
Methods — techniques the papers use, named apart from their topics
textured decomposition · 0.0parallel algorithm · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1995 | Exact Convergence of a Parallel Textured Algorithm for Data Network Optimal Routing ProblemsabstractIn our earlier paper (1991), a textured decomposition based algorithm is developed to solve the optimal routing problem in data networks; a few examples were used to illustrate the speedup advantage and the convergence conditions for the textured algorithm to converge to a global minimum. The speedup advantage is investigated in Huang et al. (1993). However, the theoretical foundation is not provided. In this paper, we provide the foundation. First, we show that for any textured decomposition, the algorithm always converges to a stationary point, which may not be a global minimum. And then, we prove that if the conditions of the exact convergence theorem are satisfied, the textured algorithm will converge to a global minimum. Garng M. Huang, Wen-Lin Hsieh |
IEEE Trans. Parallel Distributed Syst. | 2 |