文件列表:

spgl1-1.9 (0, 2015-04-30)
spgl1-1.9\ChangeLog (4528, 2015-04-30)
spgl1-1.9\Contents.m (697, 2015-04-30)
spgl1-1.9\COPYING (26436, 2015-04-30)
spgl1-1.9\NormGroupL2_dual.m (184, 2015-04-30)
spgl1-1.9\NormGroupL2_primal.m (176, 2015-04-30)
spgl1-1.9\NormGroupL2_project.m (375, 2015-04-30)
spgl1-1.9\NormL12_dual.m (221, 2015-04-30)
spgl1-1.9\NormL12_primal.m (209, 2015-04-30)
spgl1-1.9\NormL12_project.m (463, 2015-04-30)
spgl1-1.9\NormL1NN_dual.m (120, 2015-04-30)
spgl1-1.9\NormL1NN_primal.m (127, 2015-04-30)
spgl1-1.9\NormL1NN_project.m (217, 2015-04-30)
spgl1-1.9\NormL1_dual.m (63, 2015-04-30)
spgl1-1.9\NormL1_primal.m (63, 2015-04-30)
spgl1-1.9\NormL1_project.m (227, 2015-04-30)
spgl1-1.9\private (0, 2015-04-30)
spgl1-1.9\private\ensure.m (1561, 2015-04-30)
spgl1-1.9\private\heap.c (6398, 2015-04-30)
spgl1-1.9\private\heap.h (3660, 2015-04-30)
spgl1-1.9\private\lsqr.m (11849, 2015-04-30)
spgl1-1.9\private\oneProjector.m (2838, 2015-04-30)
spgl1-1.9\private\oneProjectorCore.c (5704, 2015-04-30)
spgl1-1.9\private\oneProjectorCore.h (1485, 2015-04-30)
spgl1-1.9\private\oneProjectorMex.c (4423, 2015-04-30)
spgl1-1.9\private\oneProjectorMex.m (3797, 2015-04-30)
spgl1-1.9\private\oneProjectorMex.mexglx (10390, 2015-04-30)
spgl1-1.9\private\oneProjectorMex.mexmaci (17664, 2015-04-30)
spgl1-1.9\private\oneProjectorMex.mexmaci64 (13640, 2015-04-30)
spgl1-1.9\private\oneProjectorMex.mexw32 (9216, 2015-04-30)
spgl1-1.9\spgdemo.m (16195, 2015-04-30)
spgl1-1.9\spgl1.m (31372, 2015-04-30)
spgl1-1.9\spgSetParms.m (4817, 2015-04-30)
spgl1-1.9\spgsetup.m (1608, 2015-04-30)
spgl1-1.9\spg_bp.m (1594, 2015-04-30)
spgl1-1.9\spg_bpdn.m (1809, 2015-04-30)
spgl1-1.9\spg_group.m (2625, 2015-04-30)
spgl1-1.9\spg_lasso.m (1626, 2015-04-30)
... ...

SPGL1: Spectral Projected Gradient for L1 minimization ------------------------------------------------------ 1. Introduction =============== Thank you for downloading the SPGL1 solver! SPGL1 is a Matlab solver for large-scale one-norm regularized least squares. It is designed to solve any of the following three problems: 1. Basis pursuit denoise (BPDN): minimize ||x||_1 subject to ||Ax - b||_2 <= sigma, 2. Basis pursuit (BP): minimize ||x||_1 subject to Ax = b 3. Lasso: minimize ||Ax - b||_2 subject to ||x||_1 <= tau, The matrix A can be defined explicily, or as an operator (i.e., a function) that return both both Ax and A'y. SPGL1 can solve these three problems in both the real and complex domains. Home page: https://www.math.ucdavis.edu/~mpf/spgl1/ 2. Quick start ============== Start Matlab and make sure the working directory is set to the directory containing the SPGL1 source files. When this is done, run >> spgdemo at the Matlab prompt. This script illustrates various uses of SPGL1: - Solve (BPDN) for some sigma > 0 - Solve (Lasso) - Solve (BP) - Solve a (BP) problem in complex variables - Sample the entire Pareto frontier (i.e., ||Ax-b||_2 vs ||x||_1) for a small test problem. 3. Installation =============== 3.1 MEX interface ------------------ A vital component of SPGL1 is a routine (oneProjector.m) for projecting vectors onto the one-norm ball. The default distribution includes a pure Matlab version of oneProjector which should work on all platforms, and also a C-version of this routine that is more efficient on large problems. Precompiled MEX interfaces to the C implementation of oneProjector are included for Windows (oneProjector.dll), Linux/x86 (oneProjector.mexglx) and MacOSX/Intel (oneProjector.mexmaci). If you need to compile the MEX interface on your own machine, run the following command at the Matlab prompt: >> spgsetup or, equivalently, change to the "private" directory and issue the command >> mex oneProjector.c oneProjector_core.c -output oneProjector -DNDEBUG If the MEX interface cannot be found, SPGL1 falls back to the slower Matlab implementation of oneProjector. 3.2 Path --------- In order to use SPGL1 from any directory other than the one containing the main spgl1 routine, add the SPGL1 package to your default path: >> addpath where is the location of spgl1.m. You can also add this command to your startup.m file. 4. References ============= The algorithm implemented by SPGL1 is described in the paper - E. van den Berg and M. P. Friedlander, "Probing the Pareto frontier for basis pursuit solutions", SIAM J. on Scientific Computing, 31(2):890-912, November 2008 - Sparse optimization with least-squares constraints E. van den Berg and M. P. Friedlander, Tech. Rep. TR-2010-02, Dept of Computer Science, Univ of British Columbia, January 2010

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