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Wen-Lin Hsieh

dblp:73/3823 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Routing and switching › routing algorithms
optimal routing
0.011995
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
0.011995
Exact Convergence of a Parallel Textured Algorithm for Data Network Optimal Routing Problems · IEEE Trans. Parallel Distributed Syst. 1995
Mathematical optimization
distributed optimization
0.011995
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
YearPublicationVenuePosition
1995 Exact Convergence of a Parallel Textured Algorithm for Data Network Optimal Routing Problems
abstract
In 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