Renaming OrderedLASSO to SLOPE and prox_Ol1.m to prox_Sl1.m

CHANGELOG

@@ -1,3 +1,14 @@
+-- Note --
+As of the move to github on Oct 2013, we will not mention
+minor changes in this changelog since minor changes
+can be viewed automatically using github's versioning tools
+----------
+
+Oct 29 2013, v1.3 release 2
+    Renaming solver_OrderedLASSO.m to solver_SLOPE.m
+    Renaming prox_Ol1.m to prox_Sl1.m
+    Renaming examples/smallscale/test_OrderedLASSO.m to ...test_SLOPE.m
+
 Oct 10 2013, v1.3
         License changed! See LICENSE for details
     New:

Contents.m

@@ -57,7 +57,7 @@
 %   prox_hinge                     - Hinge-loss function.
 %   prox_hingeDual                 - Dual function of the Hinge-loss function.
 %   prox_l1                        - L1 norm.
-%   prox_Ol1                       - Ordered L1 norm.
+%   prox_Sl1                       - Sorted (aka ordered) L1 norm.
 %   prox_l1l2                      - L1-L2 block norm: sum of L2 norms of rows.
 %   prox_l1linf                    - L1-LInf block norm: sum of L2 norms of rows.
 %   prox_l1pos                     - L1 norm, restricted to x >= 0
@@ -87,7 +87,7 @@
 % Premade solvers for specific problems (vector variables)
 %   solver_L1RLS                   - l1-regularized least squares problem, sometimes called the LASSO.
 %   solver_LASSO                   - Minimize residual subject to l1-norm constraints.
-%   solver_OrderedLASSO            - LASSO using ordered l1-norm.
+%   solver_SLOPE                   - Sorted L One Penalized Estimation (LASSO using sorted/ordered l1 norm)
 %   solver_sBP                     - Basis pursuit (l1-norm with equality constraints). Uses smoothing.
 %   solver_sBPDN                   - Basis pursuit de-noising. BP with relaxed constraints. Uses smoothing.
 %   solver_sBPDN_W                 - Weighted BPDN problem. Uses smoothing.

examples/smallscale/test_OrderedLASSO.m → examples/smallscale/test_SLOPE.m

@@ -1,5 +1,6 @@
 %{
-    Tests the Ordered LASSO ( aka Ordered L1 regularized Least Squares) problem
+    Tests the Sorted/Ordered LASSO ( aka Ordered L1 regularized Least Squares) problem
+    Also known as SLOPE for Sorted L-One Penalized Estimation
 
     min_x sum(lambda*sort(abs(x),'descend')) + .5||A(x)-b||_2^2
 
@@ -23,7 +24,7 @@ min_x sum(lambda*sort(abs(x),'descend')) + .5||A(x)-b||_2^2
 %%
 % Try to load the problem from disk
 fileName = fullfile(tfocs_where,...
-    'examples','smallscale','reference_solutions','ordered_asso_problem1_noisy');
+    'examples','smallscale','reference_solutions','ordered_lasso_problem1_noisy');
 randn('state',34324);
 rand('state',34324);
 N = 1024;
@@ -57,7 +58,7 @@ min_x sum(lambda*sort(abs(x),'descend')) + .5||A(x)-b||_2^2
 
     % We cannot get the solution via CVX easily, so solve using our method
     opts = struct('restart',-Inf,'tol',1e-13,'maxits',1000, 'printEvery',10);
-    [ x, out, optsOut ] = solver_OrderedLASSO( A, b, lambda, x0, opts );
+    [ x, out, optsOut ] = solver_SLOPE( A, b, lambda, x0, opts );
     x_ref       = x;
     obj_ref     = norm(A*x-b)^2/2 + sum(lambda(:).*sort(abs(x),'descend'));
 
@@ -85,7 +86,7 @@ min_x sum(lambda*sort(abs(x),'descend')) + .5||A(x)-b||_2^2
 opts.errFcn     = { @(f,primal) er(primal), ...
                     @(f,primal) f - obj_ref   }; 
 tic;
-[ x, out, optsOut ] = solver_OrderedLASSO( A, b, lambda, x0, opts );
+[ x, out, optsOut ] = solver_SLOPE( A, b, lambda, x0, opts );
 time_TFOCS = toc;
 
 fprintf('Solution has %d nonzeros.  Error vs. reference solution is %.2e\n',...

prox_Ol1.m → prox_Sl1.m

@@ -1,5 +1,5 @@
-function op = prox_Ol1( lambda )
-%PROX_OL1    Ordered L1 norm.
+function op = prox_Sl1( lambda )
+%PROX_SL1    Sorted/Ordered L1 norm.
 %    OP = PROX_L1( lambda ) implements the nonsmooth function
 %        OP(X) = sum(lambda.*sort(abs(X),'descend'))
 %    where lambda is strictly positive and sorted in decreasing order,
@@ -28,7 +28,7 @@
     error( 'Argument lambda must be sorted in decreasing order.');
 end
 if numel(lambda)==1
-    warning('TFOCS:prox_OL1','When lambda is a scalar, we recommend prox_l1.m instead pf prox_OL1.m');
+    warning('TFOCS:prox_SL1','When lambda is a scalar, we recommend prox_l1.m instead pf prox_SL1.m');
 end
 
 
@@ -39,7 +39,7 @@
     makeMex;
     % check that it worked
     if 3 ~= exist('proxAdaptiveL1Mex','file')
-        disp('Compilation of mex files for prox_OL1.m failed; please report this error');
+        disp('Compilation of mex files for prox_SL1.m failed; please report this error');
     end
 end
 

solver_OrderedLASSO.m → solver_SLOPE.m

@@ -1,18 +1,21 @@
-function [ x, odata, opts ] = solver_OrderedLASSO( A, b, lambda, x0, opts )
-% SOLVER_ORDEREDLASSO l1-regularized least squares problem, sometimes called the LASSO,
-% [ beta, odata, opts ] = solver_L1RLS( X, y, lambda, beta0, opts )
-%    Solves the l1-regularized least squares problem, using the ordered l1 norm, 
+function [ x, odata, opts ] = solver_SLOPE( A, b, lambda, x0, opts )
+% SOLVER_SLOPE Sorted l1-regularized least squares problem, 
+% [ beta, odata, opts ] = solver_SLOPE( X, y, lambda, beta0, opts )
+%    Solves the l1-regularized least squares problem, using the sorted/ordered l1 norm, 
 %        minimize (1/2)*norm( A * x - b )^2 + norm( lasso.*sort(abs(x),'descend'), 1 )
 %    using the Auslender/Teboulle variant with restart. X must be a matrix
 %    or a linear operator, y must be a vector, and lambda must be a real
 %    positive vector in decreasing order. 
 %    The initial point beta0 and option structure opts are both optional.
+%
+% SLOPE stands for Sorted L-One Penalized Estimation
+%
 % Reference:
 %   "Statistical Estimation and Testing via the Ordered l1 Norm"
 %   by M. Bogdan, E. van den Berg, W. Su, and E. J. Candès, 2013
 %   http://www-stat.stanford.edu/~candes/OrderedL1/
 %
-%   See also solver_L1RLS.m, solver_LASSO.m, prox_Ol1.m
+%   See also solver_L1RLS.m, solver_LASSO.m, prox_Sl1.m
 
 error(nargchk(3,5,nargin));
 if nargin < 4, x0 = []; end
@@ -21,7 +24,7 @@
     opts.restart = 100; 
 end
 
-[x,odata,opts] = tfocs( smooth_quad, { A, -b }, prox_Ol1( lambda ), x0, opts );
+[x,odata,opts] = tfocs( smooth_quad, { A, -b }, prox_Sl1( lambda ), x0, opts );
 
 % TFOCS v1.3 by Stephen Becker, Emmanuel Candes, and Michael Grant.
 % Copyright 2013 California Institute of Technology and CVX Research.

userguide.tex

@@ -1729,7 +1729,7 @@ \section{Appendix: list of TFOCS functions}
 \verb@prox_hinge@ & Hinge-loss function. \\
 \verb@prox_hingeDual@ & Dual function of the Hinge-loss function. \\
 \verb@prox_l1@ & L1 norm. \\
-\verb@prox_Ol1@ & Ordered L1 norm. \\
+\verb@prox_Sl1@ & Sorted (aka) ordered L1 norm. \\
 \verb@prox_l1l2@ & L1-L2 block norm: sum of L2 norms of rows. \\
 \verb@prox_l1linf@ & L1-LInf block norm: sum of L2 norms of rows. \\
 \verb@prox_l1pos@ & L1 norm, restricted to $x \ge 0$ \\
@@ -1759,7 +1759,7 @@ \section{Appendix: list of TFOCS functions}
 \multicolumn{2}{l}{\bf  Premade solvers for specific problems (vector variables)}\\
 \verb@solver_L1RLS@ & l1-regularized least squares problem, sometimes called the LASSO. \\
 \verb@solver_LASSO@ & Minimize residual subject to l1-norm constraints. \\
-\verb@solver_OrderedLASSO@ & Like LASSO but with an ordered l1 norm; see documentation. \\
+\verb@solver_SLOPE@ & Sorted L One Penalized Estimation; like LASSO but with an ordered l1 norm; see documentation. \\
 \verb@solver_sBP@ & Basis pursuit (l1-norm with equality constraints). Uses smoothing. \\
 \verb@solver_sBPDN@ & Basis pursuit de-noising. BP with relaxed constraints. Uses smoothing. \\
 \verb@solver_sBPDN_W@ & Weighted BPDN problem. Uses smoothing. \\