Title :
Sequential greedy approximation for certain convex optimization problems
Author_Institution :
IBM T. J. Watson Res. Center, Yorktown Heights, NY, USA
fDate :
3/1/2003 12:00:00 AM
Abstract :
A greedy algorithm for a class of convex optimization problems is presented. The algorithm is motivated from function approximation using a sparse combination of basis functions as well as some of its variants. We derive a bound on the rate of approximate minimization for this algorithm, and present examples of its application. Our analysis generalizes a number of earlier studies.
Keywords :
algorithm theory; convergence of numerical methods; function approximation; optimisation; approximate minimization rate; basis functions; convex optimization problems; function approximation; greedy algorithm; mixture density estimation; sequential greedy approximation; Algorithm design and analysis; Approximation algorithms; Boosting; Function approximation; Greedy algorithms; Helium; Hilbert space; Libraries; Minimization methods; Neural networks;
Journal_Title :
Information Theory, IEEE Transactions on
DOI :
10.1109/TIT.2002.808136
