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UAI
2000
15 years 8 months ago
PEGASUS: A policy search method for large MDPs and POMDPs
We propose a new approach to the problem of searching a space of policies for a Markov decision process (MDP) or a partially observable Markov decision process (POMDP), given a mo...
Andrew Y. Ng, Michael I. Jordan
CDC
2008
IEEE
145views Control Systems» more  CDC 2008»
15 years 7 months ago
Necessary and sufficient conditions for success of the nuclear norm heuristic for rank minimization
Minimizing the rank of a matrix subject to constraints is a challenging problem that arises in many applications in control theory, machine learning, and discrete geometry. This c...
Benjamin Recht, Weiyu Xu, Babak Hassibi
CORR
2010
Springer
144views Education» more  CORR 2010»
15 years 6 months ago
Efficient Minimization of Decomposable Submodular Functions
Many combinatorial problems arising in machine learning can be reduced to the problem of minimizing a submodular function. Submodular functions are a natural discrete analog of co...
Peter Stobbe, Andreas Krause
EOR
2006
88views more  EOR 2006»
15 years 6 months ago
Nonessential objectives within network approaches for MCDM
In Gal and Hanne [Eur. J. Oper. Res. 119 (1999) 373] the problem of using several methods to solve a multiple criteria decision making (MCDM) problem with linear objective functio...
Tomas Gal, Thomas Hanne
AEI
2005
99views more  AEI 2005»
15 years 6 months ago
Comparison among five evolutionary-based optimization algorithms
Evolutionary algorithms (EAs) are stochastic search methods that mimic the natural biological evolution and/or the social behavior of species. Such algorithms have been developed ...
Emad Elbeltagi, Tarek Hegazy, Donald E. Grierson