On the Convergence of SVMTorch, an Algorithm for Large-Scale Regression Problems
Ronan Collobert, Samy Bengio · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · 2000
Recently, many researchers have proposed decomposition algorithms for SVM regression problems (see for instance [11, 3, 6, 10]). In a previous paper [1], we also proposed such an algorithm, named SVMTorch. In this paper, we show that while there is actually no convergence proof for any other decomposition algorithm for SVM regression problems to our knowledge, such a proof does exist for SVMTorch for the particular case where no shrinking is used and the size of the working set is equal to 2, which is the size that gave the fastest results on most experiments we have done. This convergence proof is in fact mainly based on the convergence proof given by Keerthi and Gilbert [4] for their SVM classication algorithm.