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SIAMJO
2008
72views more  SIAMJO 2008»
14 years 10 months ago
A Sample Approximation Approach for Optimization with Probabilistic Constraints
We study approximations of optimization problems with probabilistic constraints in which the original distribution of the underlying random vector is replaced with an empirical dis...
James Luedtke, Shabbir Ahmed
LICS
2009
IEEE
15 years 4 months ago
The Inverse Taylor Expansion Problem in Linear Logic
Linear Logic is based on the analogy between algebraic linearity (i.e. commutation with sums and scalar products) and the computer science linearity (i.e. calling inputs only once...
Michele Pagani, Christine Tasson
AAIM
2008
Springer
138views Algorithms» more  AAIM 2008»
15 years 1 days ago
Confidently Cutting a Cake into Approximately Fair Pieces
We give a randomized protocol for the classic cake cutting problem that guarantees approximate proportional fairness, and with high probability uses a linear number of cuts.
Jeff Edmonds, Kirk Pruhs, Jaisingh Solanki
FOCS
2005
IEEE
15 years 3 months ago
Sampling-based Approximation Algorithms for Multi-stage Stochastic
Stochastic optimization problems provide a means to model uncertainty in the input data where the uncertainty is modeled by a probability distribution over the possible realizatio...
Chaitanya Swamy, David B. Shmoys
DAGSTUHL
2007
14 years 11 months ago
Sampling-based Approximation Algorithms for Multi-stage Stochastic Optimization
Stochastic optimization problems provide a means to model uncertainty in the input data where the uncertainty is modeled by a probability distribution over the possible realizatio...
Chaitanya Swamy, David B. Shmoys