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» Iterative methods for Robbins problems
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GECCO
2004
Springer
131views Optimization» more  GECCO 2004»
15 years 5 months ago
PolyEDA: Combining Estimation of Distribution Algorithms and Linear Inequality Constraints
Estimation of distribution algorithms (EDAs) are population-based heuristic search methods that use probabilistic models of good solutions to guide their search. When applied to co...
Jörn Grahl, Franz Rothlauf
96
Voted
IJHPCA
2006
109views more  IJHPCA 2006»
15 years 14 days ago
A Resource Leasing Policy for on-Demand Computing
Leasing computational resources for on-demand computing is now a viable option for providers of network services. Temporary spikes or lulls in demand for a service can be accommod...
Darin England, Jon B. Weissman
MOR
2002
94views more  MOR 2002»
15 years 4 days ago
The Complexity of Generic Primal Algorithms for Solving General Integer Programs
ngly better objective function value until an optimal solution is reached. From an abstract point of view, an augmentation problem is solved in each iteration. That is, given a fea...
Andreas S. Schulz, Robert Weismantel
100
Voted
ISPD
2005
ACM
151views Hardware» more  ISPD 2005»
15 years 6 months ago
Thermal via placement in 3D ICs
As thermal problems become more evident, new physical design paradigms and tools are needed to alleviate them. Incorporating thermal vias into integrated circuits (ICs) is a promi...
Brent Goplen, Sachin S. Sapatnekar
ECCV
2010
Springer
15 years 20 days ago
MIForests: Multiple-Instance Learning with Randomized Trees
Abstract. Multiple-instance learning (MIL) allows for training classifiers from ambiguously labeled data. In computer vision, this learning paradigm has been recently used in many ...
Christian Leistner, Amir Saffari, Horst Bischof