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Large-scale optimization problems abound in data mining and machine learning applications, and the computational challenges they pose are often addressed through parallelization. We identify structural properties under which a convex optimization problem can be massively parallelized via map-reduce operations using the Frank-Wolfe (FW) algorithm. The class of problems that can be tackled this way...
This paper considers the uplink of multiuser multiple-input multiple-output (MU-MIMO) systems, where, several mobile stations (MSs) cooperatively transmit hybrid messages, including common messages and private messages, to a single base station (BS). We aim to jointly design transmit precoding at the MSs side and antenna selection at the BS side to maximize the achievable system throughput. The problem...
Iterative-convergent algorithms represent an important family of applications in big data analytics. These are typically run on distributed processing frameworks deployed on a cluster of machines. On the other hand, we are witnessing the move towards data center operating systems (OS), where resources are unified by a resource manager and processing frameworks coexist with each other. In this context,...
The Frank-Wolfe algorithm has been extensively adopted in recent decades to solve the user-equilibrium problem because of its simple structure and low memory requirements. However, Dial observed that the results obtained by the Frank-Wolfe algorithm differed markedly from the B algorithm of Dial in terms of link flows, and the results obtained via Frank-Wolfe algorithm could be incorrect. This study...
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