We extend Clarkson's framework by considering parameterized convex optimization problems over the unit simplex, that depend on one parameter. We provide a simple and efficient scheme for maintaining an ε-approximate solution (and a corresponding ε-coreset) along the entire parameter path. We prove correctness and optimality of the method. Practically relevant instances of the abstract parameterized optimization problem are for example regularization paths of support vector machines, multiple kernel learning, and minimum enclosing balls of moving points. © 2010 Springer-Verlag.
CITATION STYLE
Giesen, J., Jaggi, M., & Laue, S. (2010). Approximating parameterized convex optimization problems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6346 LNCS, pp. 524–535). https://doi.org/10.1007/978-3-642-15775-2_45
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