We propose a generalized variant of Frank-Wolfe algorithm for solving a class ofsparse/low-rank optimization problems.

In this paper, we introduce alowrank interaction and sparse additive effects(LORIS) model which combines matrix regression on a dictionary and low-rank design, to estimate main effects andinteractions simultaneously.

One of the main reasons for their extensive applicability is that they comprise a flexible optimization framework which can simultaneously account for loss function minimization, saddle-point, game-theoretic, and fixed point problems.

This is an idealization of the usual way to train neural networks with a large hidden layer.

However, the optimization aspect is not sufficient to achieve the success of stochastic minimax optimizationinmachinelearning.

Risk management in dynamic decision problems is a primary concern in many fields, including financial investment, autonomous driving, and healthcare.

Weight matrix and connectivity measure.Assumencomputing nodes are connected by some

For finding a nearly second-orderstationary pointxsuchthatk F(x)k and 2F(x) I (in high probability), the best time complexity of the presented algorithms is eO(d/3.5),whereF(

Regularized empirical risk minimization problem with linear predictor appears frequentlyinmachinelearning.

Specifically, we extend the Catalyst approach originally designed fordeterministic objectives tothe stochastic setting.