Optimization algorithms for big data with application in wireless networks

This chapter proposes the use of modern first-order large-scale optimization techniques to manage a cloudbased densely deployed next-generation wireless network. In the first part of the chapter we survey a few popular first-order methods for large-scale optimization, including the block coordinate descent (BCD) method, the block successive upper-bound minimization (BSUM) method and the alternating direction method of multipliers (ADMM). In the second part of the chapter, we show that many difficult problems in managing large wireless networks can be solved efficiently and in a parallel manner, by modern first-order optimization methods. Extensive numerical results are provided to demonstrate the benefit of the proposed approach. Disciplines Signal Processing | Systems and Communications | Systems Engineering Comments This is a chapter published as Mingyi Hong, Wei-Cheng Liao, Ruoyu Sun and Zhi-Quan Luo "Optimization Algorithms for Big Data with Application in Wireless Networks," in Big Data over Networks, ed. Shuguang Cui, Alfred O. Hero III, Zhi-quan Luo, and Jose M. F. Moura (Cambridge: Cambridge University Press, 2016), pp. 66-100. Posted with permission. This book chapter is available at Iowa State University Digital Repository: https://lib.dr.iastate.edu/imse_pubs/171 I 3 Optimization algorithms for big data with application in wireless networks Mingyi Hong, Wei-Cheng Liao, Ruoyu Sun, and Zhi-Quan Luo This chapter proposes the use of modern first-order large-scale optimization techniques to manage a cloud-based densely deployed next-generation wireless network. In the first part of the chapter we survey a few popular first-order methods for large-scale optimization, including the block coordinate descent (BCD) method, the block successive upper-bound minimization (BSUM) method and the alternating direction method of multipliers (ADMM). In the second part of the chapter, we show that many difficult problems in managing large wireless networks can be solved efficiently and in a parallel manner, by modern first-order optimization methods. Extensive numerical results are provided to demonstrate the benefit of the proposed approach.