We study the problem of learning high dimensional regression models regularized by a structured-sparsity-inducing penalty that encodes prior structural information on either input...
Xi Chen, Qihang Lin, Seyoung Kim, Jaime G. Carbone...
We present an interior-point penalty method for nonlinear programming (NLP), where the merit function consists of a piecewise linear penalty function (PLPF) and an 2-penalty functi...
We present a method for propagating linear constraints. Our technique exploits the fact that the interior point method converges on a central point of the polytope. A variable assi...