References
- Chen C, He B, Ye Y, et al. The direct extension of ADMM for multi-block convex minimization problems is not necessarily convergent. Math Program Ser A. 2016;155(1–2):57–79. doi: 10.1007/s10107-014-0826-5
- Li M, Sun D, Toh K-C. A convergent 3-block semi-proximal ADMM for convex minimization problems with one strongly convex block. Asia-Pac J Oper Res. 2015;32(04).
- Cai X, Han D, Yuan X. On the convergence of the direct extension of ADMM for three-block separable convex minimization models with one strongly convex function. Comput Optim Appl. 2017;66(1):39–73. doi: 10.1007/s10589-016-9860-y
- He B, Tao M, Yuan X. Alternating direction method with Gaussian back substitution for separable convex programming. SIAM J Optim. 2012;22:313–340. doi: 10.1137/110822347
- He B,Yuan X. A class of ADMM-based algorithms for multi-block separable convex programming. Manuscript. 2015.
- Higham NJ. Computing the nearest correlation matrix a problem from finance. SIAM J Numer Anal. 2002;22:329–343. doi: 10.1093/imanum/22.3.329
- Qi H, Sun D. An augmented Lagrangian dual approach for the H-weighted nearest correlation matrix problem. IMA J Numer Anal. 2011;31(2):491–511. doi: 10.1093/imanum/drp031
- Chang X, Liu S. A 2-block semi-proximal ADMM for solving the H-weighted nearest correlation matrix problem. Optimization. 2017;66(1):1–16. doi: 10.1080/02331934.2016.1246547
- Chang X, Liu S, Li X. Modified alternating direction method of multipliers for convex quadratic semidefinite programming. Neurocomputing. 2016;214:575–586. doi: 10.1016/j.neucom.2016.06.043
- Li X, Sun D, Toh K-C. A Schur complement based semi-proximal ADMM for convex quadratic conic programming and extensions. Math Program. 2014;155(1–2):333–373. doi: 10.1007/s10107-014-0850-5
- Jiang K, Sun D, Toh K-C. An inexact accelerated proximal gradient method for large scale linearly constrained convex SDP. SIAM J Optim. 2012;22:1042–1064. doi: 10.1137/110847081
- Wen Z, Goldfarb D, Yin W, Alternating direction augmented Lagrangian methods for semidefinite programming. Math Program Comput. 2010;2:203–230. doi: 10.1007/s12532-010-0017-1
- Monteiro R, Ortiz C, Svaiter B. A first-order block-decomposition method for solving two-easy-block structured semidefinite programs. Math Program Comput. 2013;6:1–48.
- Yang L, Sun D, Toh K-C. SDPNAL+: a majorized semismooth Newton-CG augmented Lagrangian method for semidefinite programming with nonnegative constraints. Math Program Comput. 2015;7(7):331–366. doi: 10.1007/s12532-015-0082-6
- Chen L, Sun D, Toh K-C. An efficient inexact symmetric Gauss-Seidel based majorized ADMM for high-dimensional convex composite conic programming. Math Program. 2017;161(1):237–270. doi: 10.1007/s10107-016-1007-5
- Sun D, Toh K-C, Yang L. A convergent 3-block semi-proximal alternating direction method of multipliers for conic programming with 4-type of constraints. SIAM J Optim. 2015;25:882–915. doi: 10.1137/140964357
- Fazel M, Pong T, Sun D, et al. Hankel matrix rank minimization with applications in system identification and realization. SIAM J Matrix Anal Appl. 2013;34:946–977. doi: 10.1137/110853996
- The homepage of Hou-Duo Qi, http://www.personal.soton.ac.uk/hdqi/.
- Zhao X, Sun D, Toh K-C. A Newton-CG augmented Lagrangian method for semidefinite programming. SIAM J Optim. 2010;20:1737–1765. doi: 10.1137/080718206
- Li L, Toh K-C. An inexact interior point method for -regularized sparse covariance selection. Math Program Comput. 2010;2:291–315. doi: 10.1007/s12532-010-0020-6
- Binary quadratic and Max cut Library, http://biqmac.uni-klu.ac.at/biqmaclib.html.