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    Metadata details:
    filename: sll_opts.m
    path of file: ./repos/CS_MoCo_LAB/reconstruction/matlab/CS_LAB_matlab/utils/utils_SLEP/SLEP/opts
    
    the code of the file until where you have to start completion: function opts = sll_opts(opts)

% Options for Sparse Learning Library
%
% Notice:
% If one or several (even all) fields are empty, sll_opts shall assign the
% default settings.
%
% If some fields of opts have been defined, sll_opts shall check the fields
% for possible errors.
%
%
% Table of Options.  * * indicates default value.
%
%% FIELD            DESCRIPTION
%% Starting point
%
% .x0               Starting point of x. 
%                   Initialized according to .init.
%
% .c0               Starting point for the intercept c (for Logistic Loss)
%                   Initialized according to .init.
%
% .init             .init specifies how to initialize x.  
%                       * 0 => .x0 is set by the function initFactor *
%                         1 => .x0 and .c0 are defined
%                         2 => .x0= zeros(n,1), .c0=0
%
%% Termination
%
% .maxIter          Maximum number of iterations.
%                       *1e4*
%
% .tol              Tolerance parameter.
%                       *1e-4*
%
% .tFlag            Flag for termination.
%                       * 0 => abs( funVal(i)- funVal(i-1) ) <= .tol *

    
function opts = sll_opts(opts)

% Options for Sparse Learning Library
%
% Notice:
% If one or several (even all) fields are empty, sll_opts shall assign the
% default settings.
%
% If some fields of opts have been defined, sll_opts shall check the fields
% for possible errors.
%
%
% Table of Options.  * * indicates default value.
%
%% FIELD            DESCRIPTION
%% Starting point
%
% .x0               Starting point of x. 
%                   Initialized according to .init.
%
% .c0               Starting point for the intercept c (for Logistic Loss)
%                   Initialized according to .init.
%
% .init             .init specifies how to initialize x.  
%                       * 0 => .x0 is set by the function initFactor *
%                         1 => .x0 and .c0 are defined
%                         2 => .x0= zeros(n,1), .c0=0
%
%% Termination
%
% .maxIter          Maximum number of iterations.
%                       *1e4*
%
% .tol              Tolerance parameter.
%                       *1e-4*
%
% .tFlag            Flag for termination.
%                       * 0 => abs( funVal(i)- funVal(i-1) ) <= .tol *
%                         1 => abs( funVal(i)- funVal(i-1) ) 
%                              <= .tol max( funVal(i-1), 1)
%                         2 => funVal(i) <= .tol
%                         3 => norm( x_i - x_{i-1}, 2) <= .tol
%                         4 => norm( x_i - x_{i-1}, 2) <= 
%                              <= .tol max( norm( x_{i-1}, 2), 1 )
%                         5 => Run the code for .maxIter iterations
%
%% Normalization
%
% .nFlag            Flag for implicit normalization of A.
%                       * 0 => Do not normalize A *
%                         1 => A=(A-repmat(mu, m, 1))*diag(nu)^{-1}
%                         2 => A=diag(nu)^{-1}*(A-repmat(mu,m,1)
%
% .mu               Row vector to be substracted from each sample.
%                           (.mu is used when .nFlag=1 or 2)
%                       If .mu is not specified, then
%                            * .mu=mean(A,1) *
%
% .nu               Weight (column) vector for normalization
%                           (.mu is used when .nFlag=1 or 2)
%                       If .nu is not specified, then
%                       * .nFlag=1 => .nu=(sum(A.^2, 1)'/m.^{0.5} *
%                       * .nFlag=2 => .nu=(sum(A.^2, 2)/n.^{0.5} *
%
%% Regularization
%
% .rFlag            Flag for regularization
%                           (.rFlag is used for the functions with "R")
%                        * 0 => lambda is the regularization parameter *
%                          1 => lambda = lambda * lambda_{max}
%                               where lambda_{max} is the maximum lambda
%                               that yields the zero solution
% .rsL2              Regularization parameter value of the squared L2 norm
%                           (.rsL2 is used only for l1 regularization)
%                        *.rsL2=0*
%                    If .rFlag=0, .rsL2 is used without scaling
%                       .rFlag=1, .rsL2=.rsL2 * lambda_{max}
%
%% Method & Line Search
% .lFlag
%
%% Grooup & Others
%
% .ind              Indices for k groups (a k+1 row vector)
%                   For group lasso only
%                   Indices for the i-th group are (ind(i)+1):ind(i+1)
%
% .q                Value of q in L1/Lq regularization
%                      *.q=2*
%
% .sWeight          The sample (positive and negative) weight
%                   For the Logistic Loss only
%                   Positive sample: .sWeight(1)
%                   Negative sample: sWeight(2)
%                   *1/m for both positive and negative samples*
%
% .gWeight          The weight for different groups
%                      *.gWeight=1*
%
% .fName            The name of the function
%
%% Copyright (C) 2009-2010 Jun Liu, and Jieping Ye
%
% You are suggested to first read the Manual.
%
% For any problem, please contact with Jun Liu via j.liu@asu.edu
%
% Last modified 7 August 2009.

%% Starting point

if isfield(opts,'init')
    if (opts.init~=0) && (opts.init~=1) && (opts.init~=2)
        opts.init=0; % if .init is not 0, 1, or 2, then use the default 0
    end