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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 7 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 ...