It is of great significance to achieve the prediction of building energy consumption. However,
machine learning, as a promising technique for many practical applications, was rarely …

Optimization problems often require the use of optimization methods that permit the
minimization or maximization of certain objective functions. Occasionally, the problems that …

Abstract The Frank-Wolfe (FW) optimization algorithm has lately re-gained popularity thanks
in particular to its ability to nicely handle the structured constraints appearing in machine …

The purpose of this survey is to serve both as a gentle introduction and a coherent overview
of state-of-the-art Frank--Wolfe algorithms, also called conditional gradient algorithms, for …

In this paper, we propose several improvements on the block-coordinate Frank-Wolfe
(BCFW) algorithm from Lacoste-Julien et al.(2013) recently used to optimize the structured …

It is well known that the gradient descent algorithm converges linearly when applied to a
strongly convex function with Lipschitz gradient. In this case, the algorithm's rate of …

Within the large scale classification problem, the stochastic gradient descent method called
PEGASOS has been successfully applied to support vector machines (SVMs). In this paper …

Multi-object tracking has been studied for decades. However, when it comes to tracking
pedestrians in extremely crowded scenes, we are limited to only few works. This is an …

Frank-Wolfe (FW) algorithms with linear convergence rates have recently achieved great
efficiency in many applications. Garber and Meshi (2016) designed a new decomposition …

In this work, a novel feature selection method for twin Support Vector Machine (SVM) is
presented. The main idea is to combine two regularizers, namely the Euclidean and infinite …