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SIAMSC
2011
219views more  SIAMSC 2011»
15 years 1 months ago
Fast Algorithms for Bayesian Uncertainty Quantification in Large-Scale Linear Inverse Problems Based on Low-Rank Partial Hessian
We consider the problem of estimating the uncertainty in large-scale linear statistical inverse problems with high-dimensional parameter spaces within the framework of Bayesian inf...
H. P. Flath, Lucas C. Wilcox, Volkan Akcelik, Judi...
JAL
2006
175views more  JAL 2006»
15 years 6 months ago
Approximations for minimum and min-max vehicle routing problems
: We consider a variety of vehicle routing problems. The input to a problem consists of a graph G = (N, E) and edge lengths l(e) e E. Customers located at the vertices have to be ...
Esther M. Arkin, Refael Hassin, Asaf Levin
COCOON
2006
Springer
15 years 10 months ago
A Polynomial-Time Approximation Algorithm for a Geometric Dispersion Problem
We consider the problem of placing a set of disks in a region containing obstacles such that no two disks intersect. We are given a bounding polygon P and a set R of possibly inte...
Marc Benkert, Joachim Gudmundsson, Christian Knaue...
INFORMS
2008
62views more  INFORMS 2008»
15 years 6 months ago
A Stochastic Approximation Method to Compute Bid Prices in Network Revenue Management Problems
We present a stochastic approximation method to compute bid prices in network revenue management problems. The key idea is to visualize the total expected revenue as a function of...
Huseyin Topaloglu
FOCS
2005
IEEE
15 years 11 months ago
How to Pay, Come What May: Approximation Algorithms for Demand-Robust Covering Problems
Robust optimization has traditionally focused on uncertainty in data and costs in optimization problems to formulate models whose solutions will be optimal in the worstcase among ...
Kedar Dhamdhere, Vineet Goyal, R. Ravi, Mohit Sing...