과제정보
This research was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government (MSIT) (NRF-2017R1E1A1A03069931).
참고문헌
- A. Beck, Quadratic Matrix Programming, SIAM J. Optim., 17 (2006), 1224-1238. https://doi.org/10.1137/05064816X
- S, Boyd, N. Parikh, E. Chu, B. Peleato and J. Eckstein, Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers, Found. Trends Machine Learning, 3(1) (2010), 1-122. https://doi.org/10.1561/2200000016
- J. Eckstein, Augmented Lagrangian and Alternating Direction Methods for Convex Optimization: A Tutorial and Some Illustrative Computational Results, RRR 32-2012, December 2012.
- J. Eckstein and D.P. Bertsekas, On the Douglas-Rachford Splitting Method and the Proximal Point Algorithm for Maximal Monotone Operators, Math. Prog., 55 (1992), 293-318. https://doi.org/10.1007/BF01581204
- J. Eckstein and W. Yao, Understanding the Convergence of the Alternating Direction Method of Multipliers: Theoretical and Computational Perspectives, Pac. J. Optim., 11(4) (2015), 619-644.
- V. Jeyakumar, G.M. Lee and N. Dinh, New Sequential Lagrange Multiplier Conditions Characterizing Optimality without Constraint Qualification for Convex Programs, SIAM J. Optim., 14 (2003), 534-547. https://doi.org/10.1137/S1052623402417699
- M.A. Krasnosel'skii, Two Remarks on the Method of Successive Approximations, Uspekhi Mat. Nauk, 10(1) (1996), 123-127.
- W.R. Mann, Mean Value Methods in Iteration, Proc. Amer. Math. Soc., 4 (1953), 506-510. https://doi.org/10.1090/S0002-9939-1953-0054846-3
- L. Vandenberghe and S. Boyd, Semidefinite Programming, SIAM Rev., 38 (1996), 49-95. https://doi.org/10.1137/1038003