of features, the elastic-net regularization [@zou2005regularization] has been successfully applied to SVM [@wang2006doubly] and logistic regression [@ryali2010sparse]. However, incorporating domain knowledge into the logistic regression formulation is not straightforward. For SVM, including such knowledge in the optimization process has been demonstrated in [@fung2003knowledge]. Recently, an Alternating Direction Method of Multipliers (ADMM) has been proposed for the elastic-net SVM (ENSVM) [@yeefficient]. ADMM is quick to find an approximate solution to the ENSVM problem, but it is known to converge very slowly to high accuracy optimal solutions [@boyd2010distributed]. The interior-point methods (IPM) for SVM are known to be able to achieve high accuracy in their solutions with a polynomial iteration complexity, and the dual SVM formulation is independent of the feature space dimensionality. However, the classical $L_2$-norm SVM is not able to perform automatic feature selection. Although the elastic-net SVM can be formulated as a QP (in the primal form), its problem size grows substantially with the feature dimensionality. Due to the need to solve a Newton system in each iteration, the efficiency of IPM quickly deteriorates as the feature dimension becomes large.

Main Contributions
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In this paper we propose a new hybrid algorithmic framework for SVM to address [*all*]{} of 