文件名称:cvx-13.04.03
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stanford大学开发用于解优化问题的系统模型，可用于解压缩感知恢复算法：BP、DS等-the system modeldeveloped by stanford University is used for the solution of optimization problems, such as compressive sensing recovery algorithm: BP, DS
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cvx
...\structures
...\..........\cvx_s_symmetric.m
...\..........\cvx_cleanup_structure.m
...\..........\cvx_s_sparse.m
...\..........\cvx_s_complex.m
...\..........\cvx_s_upper_hankel.m
...\..........\cvx_create_structure.m
...\..........\cvx_s_skew_symmetric.m
...\..........\cvx_s_lower_hessenberg.m
...\..........\cvx_replicate_structure.m
...\..........\cvx_s_banded.m
...\..........\cvx_s_upper_hessenberg.m
...\..........\cvx_s_symmetric_ut.m
...\..........\cvx_s_lower_triangular.m
...\..........\cvx_s_lower_bidiagonal.m
...\..........\Contents.m
...\..........\@cvx
...\..........\....\structures.m
...\..........\cvx_s_hankel.m
...\..........\cvx_s_diagonal.m
...\..........\cvx_s_tridiagonal.m
...\..........\cvx_s_upper_bidiagonal.m
...\..........\cvx_s_hermitian.m
...\..........\cvx_orthog_structure.m
...\..........\cvx_invert_structure.m
...\..........\cvx_s_scaled_identity.m
...\..........\cvx_s_upper_triangular.m
...\..........\cvx_s_toeplitz.m
...\cvx_version.m
...\keywords
...\........\hypograph.m
...\........\minimize.m
...\........\subject.m
...\........\In.m
...\........\variables.m
...\........\expressions.m
...\........\minimise.m
...\........\Contents.m
...\........\dual.m
...\........\maximise.m
...\........\variable.m
...\........\maximize.m
...\........\expression.m
...\........\epigraph.m
...\sets
...\....\complex_lorentz.m
...\....\semidefinite.m
...\....\nonneg_poly_coeffs.m
...\....\geo_mean_cone.m
...\....\simplex.m
...\....\hermitian_semidefinite.m
...\....\nonnegative.m
...\....\Contents.m
...\....\rotated_lorentz.m
...\....\exponential.m
...\....\rotated_complex_lorentz.m
...\....\convex_poly_coeffs.m
...\....\lorentz.m
...\....\norm_ball.m
...\COPYING.txt
...\examples
...\........\min_phase_spectral_fact.m
...\........\examples.css
...\........\bullet.gif
...\........\simple_LS.m
...\........\sparse_heuristics
...\........\.................\sparse_solution.m
...\........\.................\sparse_infeas_dual.m
...\........\.................\html
...\........\.................\....\sparse_infeas.html
...\........\.................\....\sparse_solution.html
...\........\.................\....\sparse_infeas_dual.html
...\........\.................\....\sparse_solution__01.png
...\........\.................\Contents.m
...\........\.................\sparse_infeas.m
...\........\regularized_norm_tradeoff.m
...\........\quickstart.m
...\........\simple_LP.m
...\........\html
...\........\....\min_phase_spectral_fact.html
...\........\....\quickstart.html
...\........\....\closest_toeplitz_psd.html
...\........\....\regularized_norm_tradeoff__02.png
...\........\....\simple_LP.html
...\........\....\equality_constr_norm_min.html
...\........\....\regularized_norm_tradeoff__01.png
...\........\....\regularized_norm_tradeoff.html
...\........\....\nonneg_matrix_fact.html
...\........\....\quickstart__01.png
...\........\....\simple_LP2.html
...\........\....\nonneg_matrix_fact__01.png
...\........\....\simple_LS.html
...\........\nonneg_matrix_fact.m
...\........\antenna_array_design
...\........\....................\spectral_fact.m
...\........\....................\ant_array_min_therm_noise.m
...\........\....................\ant_array_min_beamwidth.m
...\........\....................\html
...\........\....................\....\ant_array_min_sidelobe__02.png