Linux changes

LADM_LRR/adm_lrr.m

@@ -105,7 +105,9 @@
  else
  Y1 = Y1 + mu*leq1;
  Y2 = Y2 + mu*leq2;
- mu = min(max_mu,mu*rho);
+ % if relChg < tol2
+ mu = min(max_mu,mu*rho);
+ % end
  end
 end
 
@@ -124,4 +126,4 @@
  x = (nw-lambda)*w/nw;
 else
  x = zeros(length(w),1);
-end
+end

Ncut_9/README.txt

@@ -0,0 +1,52 @@
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Normalized Cut Segmentation Code %
+% % 
+% Timothee Cour (INRIA), Stella Yu (Berkeley), Jianbo Shi (UPENN) %
+% %
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+License
+This software is made publicly for research use only. It may be modified and redistributed under the terms of the GNU General Public License. 
+
+Citation
+Please cite the following if you plan to use the code in your own work: 
+* Normalized Cuts and Image Segmentation, Jianbo Shi and Jitendra Malik, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 2000
+* Normalized Cut Segmentation Code, Timothee Cour, Stella Yu, Jianbo Shi. Copyright 2004 University of Pennsylvania, Computer and Information Science Department.
+
+Tested on matlab R2009b.
+
+Installation Notes :
+
+1) After you unzipped the files to mydir, 
+ put the Current Directory in Matlab to mydir
+
+2) In the matlab command prompt,
+ type compileDir_simple to compile the mex files (ignore the error on the C++ non-mex file; needs to be done once)
+
+3) You can now try any of the functions
+
+type demoNcutImage to see a demo of image segmentation
+type demoNcutClustering to see a demo of point cloud clustering
+
+
+Other top level functions:
+
+NcutImage.m: given image "I", segment it into "nbSegments" segments
+ [SegLabel,NcutDiscrete,NcutEigenvectors,NcutEigenvalues,W]= NcutImage(I,nbSegments);
+
+ICgraph.m: compute Intervening Contour based pixel similarity matrix W
+ W = ICgraph(I);
+
+ncutW.m: Given a similarity graph "W", computes Ncut clustering on the graph into "nbSegments" groups;
+ [NcutDiscrete,NcutEigenvectors,NcutEigenvalues] = ncutW(W,nbSegments);
+
+
+Release notes:
+
+2010, January 22: release of all c++ source mex files compatible with matlab R2009b
+2006, May 04: release version 8: fixed incompatibility issues with new matlab
+2004, June 18: release version 7: initial release
+
+Maintained by Timothee Cour, timothee dot cour at gmail dot com
+
+January 22, 2010.

Ncut_9/eigs_new.m

@@ -237,7 +237,7 @@
  t0 = cputime; % start timing ARPACK calls **aupd
 
  if isrealprob
- arpackc( aupdfun, ido, ...
+ [ido, info] = arpackc( aupdfun, ido, ...
  bmat, intconvert(n), whch, nev, tol, resid, ncv, ...
  v, ldv, iparam, ipntr, workd, workl, lworkl, info );
  else
@@ -316,7 +316,7 @@
 
 if isrealprob
  if issymA
- arpackc( eupdfun, rvec, 'A', select, ...
+  [ido,info]=arpackc( eupdfun, rvec, 'A', select, ...
  d, v, ldv, sigma, ...
  bmat, intconvert(n), whch, nev, tol, resid, ncv, ...
  v, ldv, iparam, ipntr, workd, workl, lworkl, info );

Ncut_9/ncut.m

@@ -78,8 +78,14 @@
 
 %warning off
 % [vbar,s,convergence] = eigs2(@mex_w_times_x_symmetric,size(P,1),nbEigenValues,'LA',options,tril(P)); 
+<<<<<<< HEAD
 % [vbar,s,convergence] = eigs_new(@mex_w_times_x_symmetric,size(P,1),nbEigenValues,'LA',options,tril(P)); 
 [vbar,s,convergence] = eigs(@mex_w_times_x_symmetric,size(P,1),nbEigenValues,'LA',options,tril(P)); 
+=======
+% [vbar,s,convergence] = eigs_new(@mex_w_times_x_symmetric,size(P,1),nbEigenValues,'LA',options,tril(P));
+P=0.5*(P+P');
+[vbar,s,convergence] = eigs(P,nbEigenValues,'LA',options); 
+>>>>>>> 4e2789888db39b134173590ff8bfd29ee6c32a79
 %warning on
 
 s = real(diag(s));

RPCA/exact_alm_rpca/PROPACK/lanbpro.txt

@@ -0,0 +1,85 @@
+LANBPRO Lanczos bidiagonalization with partial reorthogonalization.
+
+LANBPRO computes the Lanczos bidiagonalization of a real 
+matrix using the with partial reorthogonalization. 
+
+[U_k,B_k,V_k,R,ierr,work] = LANBPRO(A,K,R0,OPTIONS,U_old,B_old,V_old) 
+[U_k,B_k,V_k,R,ierr,work] = LANBPRO('Afun','Atransfun',M,N,K,R0, ...
+ OPTIONS,U_old,B_old,V_old) 
+
+Computes K steps of the Lanczos bidiagonalization algorithm with partial 
+reorthogonalization (BPRO) with M-by-1 starting vector R0, producing a 
+lower bidiagonal K-by-K matrix B_k, an N-by-K matrix V_k, an M-by-K 
+matrix U_k and a M-by-1 vector such that
+ A*V_k = U_k*B_k + R
+Partial reorthogonalization is used to keep the columns of V_K and U_k
+semiorthogonal:
+ MAX(DIAG((EYE(K) - V_K'*V_K))) <= OPTIONS.delta 
+and 
+ MAX(DIAG((EYE(K) - U_K'*U_K))) <= OPTIONS.delta.
+
+B_k = LANBPRO(...) returns the bidiagonal matrix only.
+
+The first input argument is either a real matrix, or a string
+containing the name of an M-file which applies a linear operator 
+to the columns of a given matrix. In the latter case, the second 
+input must be the name of an M-file which applies the transpose of 
+the same linear operator to the columns of a given matrix, 
+and the third and fourth arguments must be M and N, the dimensions 
+of then problem.
+
+The OPTIONS structure is used to control the reorthogonalization:
+ OPTIONS.delta: Desired level of orthogonality 
+ (default = sqrt(eps/K)).
+ OPTIONS.eta : Level of orthogonality after reorthogonalization 
+ (default = eps^(3/4)/sqrt(K)).
+ OPTIONS.cgs : Flag for switching between different reorthogonalization
+ algorithms:
+ 0 = iterated modified Gram-Schmidt (default)
+ 1 = iterated classical Gram-Schmidt 
+ OPTIONS.elr : If OPTIONS.elr = 1 (default) then extended local
+ reorthogonalization is enforced.
+ OPTIONS.onesided
+ : If OPTIONS.onesided = 0 (default) then both the left
+ (U) and right (V) Lanczos vectors are kept 
+ semiorthogonal. 
+ OPTIONS.onesided = 1 then only the columns of U are
+ are reorthogonalized.
+ OPTIONS.onesided = -1 then only the columns of V are
+ are reorthogonalized.
+ OPTIONS.waitbar
+ : The progress of the algorithm is display graphically.
+
+If both R0, U_old, B_old, and V_old are provided, they must
+contain a partial Lanczos bidiagonalization of A on the form
+
+ A V_old = U_old B_old + R0 . 
+
+In this case the factorization is extended to dimension K x K by
+continuing the Lanczos bidiagonalization algorithm with R0 as a 
+starting vector.
+
+The output array work contains information about the work used in
+reorthogonalizing the u- and v-vectors.
+ work = [ RU PU ]
+ [ RV PV ] 
+where
+ RU = Number of reorthogonalizations of U.
+ PU = Number of inner products used in reorthogonalizing U.
+ RV = Number of reorthogonalizations of V.
+ PV = Number of inner products used in reorthogonalizing V.
+
+References: 
+R.M. Larsen, Ph.D. Thesis, Aarhus University, 1998.
+
+G. H. Golub & C. F. Van Loan, "Matrix Computations",
+3. Ed., Johns Hopkins, 1996. Section 9.3.4.
+
+B. N. Parlett, ``The Symmetric Eigenvalue Problem'', 
+Prentice-Hall, Englewood Cliffs, NJ, 1980.
+
+H. D. Simon, ``The Lanczos algorithm with partial reorthogonalization'',
+Math. Comp. 42 (1984), no. 165, 115--142.
+
+
+Rasmus Munk Larsen, DAIMI, 1998.

RPCA/exact_alm_rpca/PROPACK/laneig.txt

@@ -0,0 +1,62 @@
+LANEIG Compute a few eigenvalues and eigenvectors.
+ LANEIG solves the eigenvalue problem A*v=lambda*v, when A is 
+ real and symmetric using the Lanczos algorithm with partial 
+ reorthogonalization (PRO). 
+
+ [V,D] = LANEIG(A) 
+ [V,D] = LANEIG('Afun',N) 
+
+ The first input argument is either a real symmetric matrix, or a 
+ string containing the name of an M-file which applies a linear 
+ operator to the columns of a given matrix. In the latter case,
+ the second input argument must be N, the order of the problem.
+
+ The full calling sequence is
+
+ [V,D,ERR] = LANEIG(A,K,SIGMA,OPTIONS)
+ [V,D,ERR] = LANEIG('Afun',N,K,SIGMA,OPTIONS)
+
+ On exit ERR contains the computed error bounds. K is the number of
+ eigenvalues desired and SIGMA is numerical shift or a two letter string
+ which specifies which part of the spectrum should be computed:
+
+ SIGMA Specified eigenvalues
+
+ 'AL' Algebraically Largest 
+ 'AS' Algebraically Smallest
+ 'LM' Largest Magnitude (default)
+ 'SM' Smallest Magnitude (does not work when A is an m-file)
+ 'BE' Both Ends. Computes k/2 eigenvalues
+ from each end of the spectrum (one more
+ from the high end if k is odd.) 
+
+ The OPTIONS structure specifies certain parameters in the algorithm.
+
+ Field name Parameter Default
+
+ OPTIONS.tol Convergence tolerance 16*eps
+ OPTIONS.lanmax Dimension of the Lanczos basis.
+ OPTIONS.v0 Starting vector for the Lanczos rand(n,1)-0.5
+ iteration.
+ OPTIONS.delta Level of orthogonality among the sqrt(eps/K)
+ Lanczos vectors.
+ OPTIONS.eta Level of orthogonality after 10*eps^(3/4)
+ reorthogonalization. 
+ OPTIONS.cgs reorthogonalization method used 0
+ '0' : iterated modified Gram-Schmidt 
+ '1' : iterated classical Gram-Schmidt
+ OPTIONS.elr If equal to 1 then extended local 1
+ reorthogonalization is enforced. 
+
+ See also LANPRO, EIGS, EIG.
+
+ References: 
+ R.M. Larsen, Ph.D. Thesis, Aarhus University, 1998.
+
+ B. N. Parlett, ``The Symmetric Eigenvalue Problem'', 
+ Prentice-Hall, Englewood Cliffs, NJ, 1980.
+
+ H. D. Simon, ``The Lanczos algorithm with partial reorthogonalization'',
+ Math. Comp. 42 (1984), no. 165, 115--142.
+
+ Rasmus Munk Larsen, DAIMI, 1998

RPCA/exact_alm_rpca/PROPACK/lanpro.txt

@@ -0,0 +1,75 @@
+LANPRO Lanczos tridiagonalization with partial reorthogonalization
+ LANPRO computes the Lanczos tridiagonalization of a real symmetric 
+ matrix using the symmetric Lanczos algorithm with partial 
+ reorthogonalization. 
+
+ [Q_K,T_K,R,ANORM,IERR,WORK] = LANPRO(A,K,R0,OPTIONS,Q_old,T_old)
+ [Q_K,T_K,R,ANORM,IERR,WORK] = LANPRO('Afun',N,K,R0,OPTIONS,Q_old,T_old)
+
+ Computes K steps of the Lanczos algorithm with starting vector R0, 
+ and returns the K x K tridiagonal T_K, the N x K matrix Q_K 
+ with semiorthonormal columns and the residual vector R such that 
+
+ A*Q_K = Q_K*T_K + R .
+
+ Partial reorthogonalization is used to keep the columns of Q_K 
+ semiorthogonal:
+ MAX(DIAG((eye(k) - Q_K'*Q_K))) <= OPTIONS.delta.
+
+
+ The first input argument is either a real symmetric matrix, a struct with
+ components A.L and A.U or a string containing the name of an M-file which 
+ applies a linear operator to the columns of a given matrix. In the latter
+ case, the second input argument must be N, the order of the problem.
+
+ If A is a struct with components A.L and A.U, such that 
+ L*U = (A - sigma*I), a shift-and-invert Lanczos iteration is performed
+
+ The OPTIONS structure is used to control the reorthogonalization:
+ OPTIONS.delta: Desired level of orthogonality 
+ (default = sqrt(eps/K)).
+ OPTIONS.eta : Level of orthogonality after reorthogonalization 
+ (default = eps^(3/4)/sqrt(K)).
+ OPTIONS.cgs : Flag for switching between different reorthogonalization
+ algorithms:
+ 0 = iterated modified Gram-Schmidt (default)
+ 1 = iterated classical Gram-Schmidt 
+ OPTIONS.elr : If OPTIONS.elr = 1 (default) then extended local
+ reorthogonalization is enforced.
+ OPTIONS.Y : The lanczos vectors are reorthogonalized against
+ the columns of the matrix OPTIONS.Y.
+
+ If both R0, Q_old and T_old are provided, they must contain 
+ a partial Lanczos tridiagonalization of A on the form
+
+ A Q_old = Q_old T_old + R0 . 
+
+ In this case the factorization is extended to dimension K x K by
+ continuing the Lanczos algorithm with R0 as starting vector.
+
+ On exit ANORM contains an approximation to ||A||_2. 
+ IERR = 0 : K steps were performed succesfully.
+ IERR > 0 : K steps were performed succesfully, but the algorithm
+ switched to full reorthogonalization after IERR steps.
+ IERR < 0 : Iteration was terminated after -IERR steps because an
+ invariant subspace was found, and 3 deflation attempts 
+ were unsuccessful.
+ On exit WORK(1) contains the number of reorthogonalizations performed, and
+ WORK(2) contains the number of inner products performed in the
+ reorthogonalizations.
+
+ See also LANEIG, REORTH, COMPUTE_INT
+
+ References: 
+ R.M. Larsen, Ph.D. Thesis, Aarhus University, 1998.
+
+ G. H. Golub & C. F. Van Loan, "Matrix Computations",
+ 3. Ed., Johns Hopkins, 1996. Chapter 9.
+
+ B. N. Parlett, ``The Symmetric Eigenvalue Problem'', 
+ Prentice-Hall, Englewood Cliffs, NJ, 1980.
+
+ H. D. Simon, ``The Lanczos algorithm with partial reorthogonalization'',
+ Math. Comp. 42 (1984), no. 165, 115--142.
+
+ Rasmus Munk Larsen, DAIMI, 1998

RPCA/exact_alm_rpca/PROPACK/lansvd.txt

@@ -0,0 +1,58 @@
+LANSVD Compute a few singular values and singular vectors.
+ LANSVD computes singular triplets (u,v,sigma) such that
+ A*u = sigma*v and A'*v = sigma*u. Only a few singular values 
+ and singular vectors are computed using the Lanczos 
+ bidiagonalization algorithm with partial reorthogonalization (BPRO). 
+
+ S = LANSVD(A) 
+ S = LANSVD('Afun','Atransfun',M,N) 
+
+ The first input argument is either a matrix or a
+ string containing the name of an M-file which applies a linear
+ operator to the columns of a given matrix. In the latter case,
+ the second input must be the name of an M-file which applies the
+ transpose of the same operator to the columns of a given matrix, 
+ and the third and fourth arguments must be M and N, the dimensions 
+ of the problem.
+
+ [U,S,V] = LANSVD(A,K,'L',...) computes the K largest singular values.
+
+ [U,S,V] = LANSVD(A,K,'S',...) computes the K smallest singular values.
+
+ The full calling sequence is
+
+ [U,S,V] = LANSVD(A,K,SIGMA,OPTIONS) 
+ [U,S,V] = LANSVD('Afun','Atransfun',M,N,K,SIGMA,OPTIONS)
+
+ where K is the number of singular values desired and 
+ SIGMA is 'L' or 'S'.
+
+ The OPTIONS structure specifies certain parameters in the algorithm.
+ Field name Parameter Default
+
+ OPTIONS.tol Convergence tolerance 16*eps
+ OPTIONS.lanmax Dimension of the Lanczos basis.
+ OPTIONS.p0 Starting vector for the Lanczos rand(n,1)-0.5
+ iteration.
+ OPTIONS.delta Level of orthogonality among the sqrt(eps/K)
+ Lanczos vectors.
+ OPTIONS.eta Level of orthogonality after 10*eps^(3/4)
+ reorthogonalization. 
+ OPTIONS.cgs reorthogonalization method used 0
+ '0' : iterated modified Gram-Schmidt 
+ '1' : iterated classical Gram-Schmidt
+ OPTIONS.elr If equal to 1 then extended local 1
+ reorthogonalization is enforced. 
+
+ See also LANBPRO, SVDS, SVD
+
+ References: 
+ R.M. Larsen, Ph.D. Thesis, Aarhus University, 1998.
+
+ B. N. Parlett, ``The Symmetric Eigenvalue Problem'', 
+ Prentice-Hall, Englewood Cliffs, NJ, 1980.
+
+ H. D. Simon, ``The Lanczos algorithm with partial reorthogonalization'',
+ Math. Comp. 42 (1984), no. 165, 115--142.
+
+ Rasmus Munk Larsen, DAIMI, 1998