You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: ConsensusAdmm.m
    path of file: ./repos/StackExchangeCodes/Mathematics/Q3599003
    
    the code of the file until where you have to start completion: function [ vX ] = ConsensusAdmm( cProxFun, numElements, paramRho, numIterations, stopThr )
% ----------------------------------------------------------------------------------------------- %
% [ vX ] = ConsensusAdmm( cProxFun, numElements, paramRho, numIterations, stopThr )
%   Solves the Consensus ADMM problem given the set of Proximal Operators.
% Input:
%   - cProxFun      -   Set of Proximal Operators.
%                       Each cell elemnts is the i-th proximal operator.
%                       Each function handler input is a (vV, paramLambda).
%                       The implementation is for the prox in its lambda
%                       form: 0.5 * || x - v ||^2 + paramLamba * g(x).
%                       Structure: Cell Array (numSets x 1).
%                       Type: Function Handler.
%                       Range: NA.
% Output:
%   - vX            -   Solution Vector.
%                       The solution to the optimization problem..
%                       Structure: Vector (m x 1).
%                       Type: 'Single' / 'Double'.
%                       Range: (-inf, inf).
% References
%   1.  ELE 522 - Large Scale Optimization for Data Science - Alternating Direction Method of Multipliers.
% Remarks:
%   1.  Pay attention that the references are using (1 / paramLambda) form
%       of the Prox. Hence the adaptation in the code.
%   2.  This is ultra efficient way to solve the Orthogonal Projection
%       Problem in case having projection onto sets which the intersection
%       is the objective set.
% TODO:
%   1.  C
% Release Notes:
%   -   1.0.000     25/03/2020  Royi Avital
%       *   First release version.
% ----------------------------------------------------------------------------------------------- %

FALSE   = 0;
TRUE    = 1;


    
function [ vX ] = ConsensusAdmm( cProxFun, numElements, paramRho, numIterations, stopThr )
% ----------------------------------------------------------------------------------------------- %
% [ vX ] = ConsensusAdmm( cProxFun, numElements, paramRho, numIterations, stopThr )
%   Solves the Consensus ADMM problem given the set of Proximal Operators.
% Input:
%   - cProxFun      -   Set of Proximal Operators.
%                       Each cell elemnts is the i-th proximal operator.
%                       Each function handler input is a (vV, paramLambda).
%                       The implementation is for the prox in its lambda
%                       form: 0.5 * || x - v ||^2 + paramLamba * g(x).
%                       Structure: Cell Array (numSets x 1).
%                       Type: Function Handler.
%                       Range: NA.
% Output:
%   - vX            -   Solution Vector.
%                       The solution to the optimization problem..
%                       Structure: Vector (m x 1).
%                       Type: 'Single' / 'Double'.
%                       Range: (-inf, inf).
% References
%   1.  ELE 522 - Large Scale Optimization for Data Science - Alternating Direction Method of Multipliers.
% Remarks:
%   1.  Pay attention that the references are using (1 / paramLambda) form
%       of the Prox. Hence the adaptation in the code.
%   2.  This is ultra efficient way to solve the Orthogonal Projection
%       Problem in case having projection onto sets which the intersection
%       is the objective set.
% TODO:
%   1.  C
% Release Notes:
%   -   1.0.000     25/03/2020  Royi Avital
%       *   First release version.
% ----------------------------------------------------------------------------------------------- %

FALSE   = 0;
TRUE    = 1;

OFF     = 0;
ON      = 1;

numSets     = size(cProxFun, 1);
% numElements = size(vX, 1);

paramRhoInv = 1 / paramRho;

mX      = zeros(numElements, numSets);
vZ      = mean(mX, 2);
mLambda = zeros(numElements, numSets);

for ii = 1:numIterations
    for jj = 1:numSets
        mX(:, jj) = cProxFun{jj}(vZ - (paramRhoInv * mLambda(:, jj)), paramRhoInv);
    end