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TVAL3算法TVAL3_beta2.4

于 2021-04-15 发布 文件大小:320KB
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  这是TVAL3算法，是基于增广拉格朗日法和交替方向法的全变分最小化算法。这个算法是有关压缩感知的重建算法的例子。TVAL3算法是在最小全变分法的基础上，结合了增强型拉格朗日函数和交替最小化方法。通过交替最小化方法寻找增强型拉格朗日模型的最小值，再由最速下降法进行迭代，更新拉格朗日乘子。 (TVAL3: TV minimization by Augmented Lagrangian and ALternating direction ALgorithms.This algorithm is an example of a reconstruction algorithm for compressed sensing. The TVAL3 algorithm is based on the minimum total variation method, which combines the enhanced Lagrange function and the alternating minimization method. The minimum value of the enhanced Lagrange model is sought by the alternate minimization method, and then the iterative method is updated by the steepest descent method.)

文件列表：

TVAL3_beta2.4
.............\TVAL3_beta2.4
.............\.............\A.mat,236,2013-12-23
.............\.............\demo.asv,1412,2013-12-22
.............\.............\demo.m,1412,2013-12-22
.............\.............\Demos
.............\.............\.....\demo_barbara.m,3196,2010-11-10
.............\.............\.....\demo_complex.m,2530,2010-11-10
.............\.............\.....\demo_DFT.m,3961,2010-11-10
.............\.............\.....\demo_phantom.m,2611,2010-11-10
.............\.............\Fast_Walsh_Hadamard_Transform
.............\.............\.............................\fWHtrans.cpp,3450,2010-02-16
.............\.............\.............................\fWHtrans.mexa64,8416,2010-04-05
.............\.............\.............................\fWHtrans.mexmaci,9284,2010-06-17
.............\.............\.............................\fWHtrans.mexmaci64,9112,2010-11-10
.............\.............\.............................\fWHtrans.mexw32,6656,2010-11-10
.............\.............\.............................\fWHtrans.mexw64,9216,2010-11-10
.............\.............\.............................\ifWHtrans.m,446,2010-02-15
.............\.............\.............................\newFWHTcmp.fig,11358,2010-02-17
.............\.............\.............................\readme.txt,2115,2010-06-17


.............\.............\Solver
.............\.............\......\ftvcs_alp.m,15273,2010-11-10
.............\.............\......\ftvcs_alp_old.m,14446,2010-11-10
.............\.............\......\ftvcs_al_opts.m,5646,2010-11-10
.............\.............\......\ftvcs_al_TVL2p.m,14704,2010-11-10
.............\.............\......\ftvcs_al_TVL2p_old.m,13827,2010-11-10
.............\.............\......\readme.txt,2834,2010-06-17
.............\.............\......\TVAL3.m,674,2010-11-09
.............\.............\test.asv,1291,2013-12-23
.............\.............\test.m,1441,2013-12-23
.............\.............\User_Guide.pdf,140176,2010-11-10
.............\.............\Utilities
.............\.............\.........\At_fWH.m,284,2010-06-17
.............\.............\.........\A_fWH.m,244,2010-06-17
.............\.............\.........\barbara256.tif,107022,2009-06-15
.............\.............\.........\defDDt.m,379,2009-02-19
.............\.............\.........\Fp1d.m,197,2008-08-27
.............\.............\.........\Fp2d.m,326,2008-11-17
.............\.............\.........\FpT1d.m,233,2008-08-27
.............\.............\.........\FpT2d.m,265,2008-11-17
.............\.............\.........\f_handleA.m,202,2009-02-17
.............\.............\.........\getA.m,590,2009-02-09
.............\.............\.........\isinInterval.m,369,2008-06-14
.............\.............\.........\MRImask.m,477,2007-11-08
.............\.............\.........\ScaleA.m,629,2009-05-20
.............\.............\.........\Scaleb.m,629,2009-05-20
.............\.............\warm_up.m,503,2010-11-10

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    In this demo, I use the EM algorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve recently derived, to train a two-layer perceptron, so as to classify medical data (kindly provided by Steve Roberts and Will Penny from EE, Imperial College). The data and simulations are described in: Nando de Freitas, Mahesan Niranjan and Andrew Gee Nonlinear State Space Estimation with Neural Networks and the EM algorithm After downloading the file, type "tar -xf EMdemo.tar" to uncompress it. This creates the directory EMdemo containing the required m files. Go to this directory, load matlab5 and type "EMtremor". The figures will then show you the simulation results, including ROC curves, likelihood plots, decision boundaries with error bars, etc. WARNING: Do make sure that you monitor the log-likelihood and check that it is increasing. Due to numerical errors, it might show glitches for some data sets. (In this demo, I use the EM algorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve recently derived, to train a two-layer perceptron, so as to classify medical data (kindly provided by Steve Roberts and Will Penny from EE, Imperial College). The data and simulations are described in: Nando de Freitas, Mahesan Niranjan and Andrew Gee Nonlinear State Space Estimation with Neural Networks and the EM algorithm After downloading the file, type "tar-xf EMdemo.tar" to uncompress it. This creates the directory EMdemo containing the required m files. Go to this directory, load matlab5 and type "EMtremor". The figures will then show you the simulation results, including ROC curves, likelihood plots, decision boundaries with error bars, etc. WARNING: Do make sure that you monitor the log-likelihood and check that it is increasing. Due to numerical errors, it might show glitches for some data sets. )
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    2016-04-12 13:38:25下载
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