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» Terminating Decision Algorithms Optimally
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EMO
2009
Springer
174views Optimization» more  EMO 2009»
15 years 10 months ago
Constraint Programming
To model combinatorial decision problems involving uncertainty and probability, we introduce stochastic constraint programming. Stochastic constraint programs contain both decision...
Pascal Van Hentenryck
127
Voted
INFOCOM
2010
IEEE
15 years 1 months ago
Overcoming Failures: Fault-tolerance and Logical Centralization in Clean-Slate Network Management
—We investigate the design of a clean-slate control and nt plane for data networks using the abstraction of 4D architecture, utilizing and extending 4D’s concept of logically c...
Hammad Iqbal, Taieb Znati
217
Voted
ICAART
2010
INSTICC
16 years 16 days ago
Complexity of Stochastic Branch and Bound Methods for Belief Tree Search in Bayesian Reinforcement Learning
There has been a lot of recent work on Bayesian methods for reinforcement learning exhibiting near-optimal online performance. The main obstacle facing such methods is that in most...
Christos Dimitrakakis
GECCO
2006
Springer
162views Optimization» more  GECCO 2006»
15 years 7 months ago
Algebraic simplification of GP programs during evolution
Program bloat is a fundamental problem in the field of Genetic Programming (GP). Exponential growth of redundant and functionally useless sections of programs can quickly overcome...
Phillip Wong, Mengjie Zhang
113
Voted
STOC
1995
ACM
145views Algorithms» more  STOC 1995»
15 years 7 months ago
Polynomial time approximation schemes for dense instances of NP-hard problems
We present a unified framework for designing polynomial time approximation schemes (PTASs) for “dense” instances of many NP-hard optimization problems, including maximum cut,...
Sanjeev Arora, David R. Karger, Marek Karpinski