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AAAI
2006
13 years 7 months ago
Point-based Dynamic Programming for DEC-POMDPs
We introduce point-based dynamic programming (DP) for decentralized partially observable Markov decision processes (DEC-POMDPs), a new discrete DP algorithm for planning strategie...
Daniel Szer, François Charpillet
ICML
2010
IEEE
13 years 6 months ago
Learning Efficiently with Approximate Inference via Dual Losses
Many structured prediction tasks involve complex models where inference is computationally intractable, but where it can be well approximated using a linear programming relaxation...
Ofer Meshi, David Sontag, Tommi Jaakkola, Amir Glo...
IOR
2011
152views more  IOR 2011»
13 years 22 days ago
Risk-Averse Two-Stage Stochastic Linear Programming: Modeling and Decomposition
We formulate a risk-averse two-stage stochastic linear programming problem in which unresolved uncertainty remains after the second stage. The objective function is formulated as ...
Naomi Miller, Andrzej Ruszczynski
CSCLP
2005
Springer
13 years 11 months ago
A Hybrid Benders' Decomposition Method for Solving Stochastic Constraint Programs with Linear Recourse
Abstract. We adopt Benders’ decomposition algorithm to solve scenariobased Stochastic Constraint Programs (SCPs) with linear recourse. Rather than attempting to solve SCPs via a ...
Armagan Tarim, Ian Miguel
CCE
2010
13 years 3 months ago
A simple heuristic for reducing the number of scenarios in two-stage stochastic programming
In this work we address the problem of solving multiscenario optimization models that are deterministic equivalents of two-stage stochastic programs. We present a heuristic approx...
Ramkumar Karuppiah, Mariano Martín, Ignacio...