I Simple proof of linear convergence. I For convex functions, equivalent to several of the above conditions. I For non-convex functions, weakest assumption while still guaranteeing global minimizer. ? We generalize the PL condition to analyze proximal-gradient methods. ? We give simple new analyses in a variety of settings: I Least-squares and logistic regression. I Randomized coordinate descen...

We present and study a distributed optimization algorithm by employing a stochastic dual coordinate ascent method. Stochastic dual coordinate ascent methods enjoy strong theoretical guarantees and often have better performances than stochastic gradient descent methods in optimizing regularized loss minimization problems. It still lacks of efforts in studying them in a distributed framework. We ...

Accelerated coordinate descent is widely used in optimization due to its cheap per-iteration cost and scalability to large-scale problems. Up to a primal-dual transformation, it is also the same as accelerated stochastic gradient descent that is one of the central methods used in machine learning. In this paper, we improve the best known running time of accelerated coordinate descent by a facto...

We propose a novel support vector regression approach called e-Distance Weighted Support Vector Regression (e-DWSVR). e-DWSVR specifically addresses two challenging issues in support vector regression: first, the process of noisy data; second, how to deal with the situation when the distribution of boundary data is different from that of the overall data. The proposed e-DWSVR optimizes the mini...

Traffic networks inherently exhibit several degrees of decomposition, both hierarchically and geographically, and traffic control has been, in practice, severely influenced by this decomposition. Anticipatory Traffic Control literature (Allsop, 1974), in its own, deals with the fact that user behavior can be, at least to some degree, predicted, and that an “optimal” controller should take this ...