交替方向乘子法ADMM算法的matlab代码

## LibADMM ### Introduction This toolbox solves many sparse, low-rank matrix and low-rank tensor optimization problems by using M-ADMM developed in our paper <a class="footnote-reference" href="#id2" id="id1">[1]</a>. ### List of Problems The table below gives the list of problems solved in our toolbox. See more details in the manual at <a href="../publications/2016-software-LibADMM.pdf" class="textlink" target="_blank">https://canyilu.github.io/publications/2016-software-LibADMM.pdf</a>. <p align="center"> <img src="https://github.com/canyilu/LibADMM/blob/master/tab_problemlist.JPG"> </p> ### Citing <p>In citing this toolbox in your papers, please use the following references:</p> <div class="highlight-none"><div class="highlight"><pre> 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. </pre></div> <p>The corresponding BiBTeX citation are given below:</p> <div class="highlight-none"><div class="highlight"><pre> @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}, }</pre></div> ### Version History - Version 1.0 was released on June, 2016. - Version 1.1 was released on June, 2018. Some key differences are below: + Add a new model about low-rank tensor recovery from Gaussian measurements based on tensor nuclear norm and the corresponding function lrtr_Gaussian_tnn.m + Update several functions to improve the efficiency, including prox_tnn.m, tprod.m, tran.m, tubalrank.m, and nmodeproduct.m + Update the three example functions: example_sparse_models.m, example_low_rank_matrix_models.m, and example_low_rank_tensor_models.m + Remove the test on image data and some unnecessary functions ### References <table class="docutils footnote" frame="void" id="id2" rules="none"> <colgroup><col class="label" /><col /></colgroup> <tbody valign="top"> <tr><td class="label"><a class="fn-backref" href="#id2">[1]</a></td><td>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</td></tr> </tbody> </table>