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Greedy approximation algorithms have been frequently used to obtain sparse solutions to learning problems. This paper presents a general greedy algorithm for solving a class of convex optimization problems. This paper derives a bound on the rate of approximation for this algorithm, and show that the algorithm includes a number of earlier studies as special cases.
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