Sciweavers

497 search results - page 17 / 100
» Approximating max-min linear programs with local algorithms
Sort
View
ICALP
2005
Springer
15 years 3 months ago
How Well Can Primal-Dual and Local-Ratio Algorithms Perform?
We define an algorithmic paradigm, the stack model, that captures many primal-dual and local-ratio algorithms for approximating covering and packing problems. The stack model is ...
Allan Borodin, David Cashman, Avner Magen
ICASSP
2010
IEEE
14 years 10 months ago
A convex relaxation for approximate maximum-likelihood 2D source localization from range measurements
This paper addresses the problem of locating a single source from noisy range measurements in wireless sensor networks. An approximate solution to the maximum likelihood location ...
Pinar Oguz-Ekim, João Pedro Gomes, Jo&atild...
IJCNN
2000
IEEE
15 years 2 months ago
Piecewise Linear Homeomorphisms: The Scalar Case
The class of piecewise linear homeomorphisms (PLH) provides a convenient functional representation for many applications wherein an approximation to data is required that is inver...
Richard E. Groff, Daniel E. Koditschek, Pramod P. ...
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