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TSP
2010
15 years 1 months ago
Methods for sparse signal recovery using Kalman filtering with embedded pseudo-measurement norms and quasi-norms
We present two simple methods for recovering sparse signals from a series of noisy observations. The theory of compressed sensing (CS) requires solving a convex constrained minimiz...
Avishy Carmi, Pini Gurfil, Dimitri Kanevsky
GECCO
2005
Springer
113views Optimization» more  GECCO 2005»
16 years 23 days ago
Healthy daily meal planner
The purpose of this project is to develop a program that solves a bi-objective diet problem to propose the user a “healthy” daily meal according to some parameters specified b...
Aynur Kahraman, H. Aydolu Seven
ICPR
2008
IEEE
16 years 1 months ago
Regression with interval output values
We consider a regression problem where target values are given as intervals, and propose a statistical approach to it. Although it is hard to solve the optimization problem direct...
Hisashi Kashima, Kazutaka Yamasaki, Akihiro Inokuc...
STOC
2012
ACM
217views Algorithms» more  STOC 2012»
13 years 9 months ago
Tight lower bounds for the online labeling problem
We consider the file maintenance problem (also called the online labeling problem) in which n integer items from the set {1, . . . , r} are to be stored in an array of size m ≥...
Jan Bulánek, Michal Koucký, Michael ...
CEC
2008
IEEE
16 years 1 months ago
Improved Particle Swarm Optimization with low-discrepancy sequences
— Quasirandom or low discrepancy sequences, such as the Van der Corput, Sobol, Faure, Halton (named after their inventors) etc. are less random than a pseudorandom number sequenc...
Millie Pant, Radha Thangaraj, Crina Grosan, Ajith ...