Boyd-admm_code _paper.zip

[{"title":"（ 48 个子文件 1.38MB ） Boyd-admm_code _paper.zip","children":[{"title":"9 L_1-regularized logistic regression","children":[{"title":"logreg_example.m <span style='color:#111;'> 1.19KB </span>","children":null,"spread":false},{"title":"logreg.m <span style='color:#111;'> 3.57KB </span>","children":null,"spread":false}],"spread":true},{"title":"2011【综述ADMM】Boyd- Distributed_Optimization_and_Statistical_Learning_.pdf <span style='color:#111;'> 822.80KB </span>","children":null,"spread":false},{"title":"5 Lasso","children":[{"title":"lasso.m <span style='color:#111;'> 2.92KB </span>","children":null,"spread":false},{"title":"lasso_example.m <span style='color:#111;'> 1.05KB </span>","children":null,"spread":false}],"spread":true},{"title":"3 Huber fitting","children":[{"title":"huber_fit_example.m <span style='color:#111;'> 1023B </span>","children":null,"spread":false},{"title":"huber_fit.m <span style='color:#111;'> 2.67KB </span>","children":null,"spread":false}],"spread":true},{"title":"13 Sum-of-norms regularization (group lasso)","children":[{"title":"group_lasso_example.m <span style='color:#111;'> 1.69KB </span>","children":null,"spread":false},{"title":"group_lasso_example_01.png <span style='color:#111;'> 4.14KB </span>","children":null,"spread":false},{"title":"group_lasso_example_02.png <span style='color:#111;'> 5.21KB </span>","children":null,"spread":false},{"title":"group_lasso.m <span style='color:#111;'> 3.48KB </span>","children":null,"spread":false}],"spread":true},{"title":"2 Sparse inverse covariance selection","children":[{"title":"covsel.m <span style='color:#111;'> 2.55KB </span>","children":null,"spread":false},{"title":"covsel_example.m <span style='color:#111;'> 1.19KB </span>","children":null,"spread":false}],"spread":true},{"title":"2012 Boyd-admm_tv_est.pdf <span style='color:#111;'> 346.83KB </span>","children":null,"spread":false},{"title":"8 Linear programming","children":[{"title":"linprog.m <span style='color:#111;'> 2.18KB </span>","children":null,"spread":false},{"title":"linprog_example.m <span style='color:#111;'> 1.02KB </span>","children":null,"spread":false}],"spread":true},{"title":"6 Lasso with LSQR, an iterative method for solving sparse systems","children":[{"title":"lasso_lsqr.m <span style='color:#111;'> 2.65KB </span>","children":null,"spread":false},{"title":"lasso_lsqr_example.m <span style='color:#111;'> 1.05KB </span>","children":null,"spread":false}],"spread":true},{"title":"2011 Boyd-admm_slides.pdf <span style='color:#111;'> 384.84KB </span>","children":null,"spread":false},{"title":"10 Distributed ell_1-regularized logistic regression","children":[{"title":"distr_l1_logreg_example.m <span style='color:#111;'> 1.33KB </span>","children":null,"spread":false},{"title":"distr_l1_logreg.m <span style='color:#111;'> 4.04KB </span>","children":null,"spread":false},{"title":"说明.txt <span style='color:#111;'> 77B </span>","children":null,"spread":false}],"spread":true},{"title":"4 Intersection of polyhedra","children":[{"title":"polyhedra_intersection_example.m <span style='color:#111;'> 2.29KB </span>","children":null,"spread":false},{"title":"polyhedra_intersection.m <span style='color:#111;'> 2.51KB </span>","children":null,"spread":false}],"spread":true},{"title":"7 Least absolute deviations","children":[{"title":"lad_example.m <span style='color:#111;'> 941B </span>","children":null,"spread":false},{"title":"lad.m <span style='color:#111;'> 2.29KB </span>","children":null,"spread":false}],"spread":true},{"title":"14 Sum-of-norms regularization (group lasso) with feature splitting","children":[{"title":"group_lasso_feat_split_example_01.png <span style='color:#111;'> 4.72KB </span>","children":null,"spread":false},{"title":"group_lasso_feat_split_example.m <span style='color:#111;'> 1.48KB </span>","children":null,"spread":false},{"title":"group_lasso_feat_split_example_02.png <span style='color:#111;'> 5.62KB </span>","children":null,"spread":false},{"title":"group_lasso_feat_split.m <span style='color:#111;'> 3.78KB </span>","children":null,"spread":false}],"spread":true},{"title":"16 Total variation minimization","children":[{"title":"total_variation_example.m <span style='color:#111;'> 1010B </span>","children":null,"spread":false},{"title":"total_variation_example_01.png <span style='color:#111;'> 3.59KB </span>","children":null,"spread":false},{"title":"total_variation_example_02.png <span style='color:#111;'> 4.70KB </span>","children":null,"spread":false},{"title":"total_variation_example_result.txt <span style='color:#111;'> 5.30KB </span>","children":null,"spread":false},{"title":"total_variation.m <span style='color:#111;'> 2.49KB </span>","children":null,"spread":false}],"spread":false},{"title":"15 Support vector machine","children":[{"title":"linear_svm_example_01.png <span style='color:#111;'> 3.60KB </span>","children":null,"spread":false},{"title":"linear_svm_example_02.png <span style='color:#111;'> 5.11KB </span>","children":null,"spread":false},{"title":"linear_svm_example_result.txt <span style='color:#111;'> 6.25KB </span>","children":null,"spread":false},{"title":"linear_svm.m <span style='color:#111;'> 3.11KB </span>","children":null,"spread":false},{"title":"linear_svm_example.m <span style='color:#111;'> 1.35KB </span>","children":null,"spread":false}],"spread":false},{"title":"12 Quadratic programming","children":[{"title":"quadprog_example_02.png <span style='color:#111;'> 4.78KB </span>","children":null,"spread":false},{"title":"quadprog_example.m <span style='color:#111;'> 1.02KB </span>","children":null,"spread":false},{"title":"quadprog.m <span style='color:#111;'> 2.33KB </span>","children":null,"spread":false},{"title":"quadprog_example_01.png <span style='color:#111;'> 3.76KB </span>","children":null,"spread":false}],"spread":false},{"title":"1 Basis pursuit","children":[{"title":"basis_pursuit.m <span style='color:#111;'> 2.35KB </span>","children":null,"spread":false},{"title":"basis_pursuit_example.m <span style='color:#111;'> 850B </span>","children":null,"spread":false}],"spread":false},{"title":"11 Regressor selection (nonconvex problem)","children":[{"title":"regressor_sel.m <span style='color:#111;'> 2.91KB </span>","children":null,"spread":false},{"title":"regressor_sel_example.m <span style='color:#111;'> 1.13KB </span>","children":null,"spread":false}],"spread":false}],"spread":true}]