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This toolbox solves many sparse, low-rank matrix and low-rank tensor optimization problems by using M-ADMM developed in our paper [1].
The table below gives the list of problems solved in our toolbox. See more details in the manual at https://canyilu.github.io/publications/2016-software-LibADMM.pdf.
In citing this toolbox in your papers, please use the following references:
C. Lu, J. Feng, S. Yan, Z. Lin. A Unified Alternating Direction Method of Multipliers by Majorization Minimization. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, pp. 527-541, 2018 C. Lu. A Library of ADMM for Sparse and Low-rank Optimization. National University of Singapore, June 2016. https://github.com/canyilu/LibADMM.
The corresponding BiBTeX citation are given below:
@manual{lu2016libadmm, author = {Lu, Canyi}, title = {A Library of {ADMM} for Sparse and Low-rank Optimization}, organization = {National University of Singapore}, month = {June}, year = {2016}, note = {\url{https://github.com/canyilu/LibADMM}} } @article{lu2018unified, author = {Lu, Canyi and Feng, Jiashi and Yan, Shuicheng and Lin, Zhouchen}, title = {A Unified Alternating Direction Method of Multipliers by Majorization Minimization}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, publisher = {IEEE}, year = {2018}, volume = {40}, number = {3}, pages = {527—-541}, }
[1] | C. Lu, J. Feng, S. Yan, Z. Lin. A Unified Alternating Direction Method of Multipliers by Majorization Minimization. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, pp. 527-541, 2018 |
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