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: ProxBoxIndicatorLInfNormReg.m
    path of file: ./repos/StackExchangeCodes/Mathematics/Q2301266
    
    the code of the file until where you have to start completion: function [ vX ] = ProxBoxIndicatorLInfNormReg( vY, paramAlpha )
% ----------------------------------------------------------------------------------------------- %
% [ vX ] = ProxL2BallIndicatorL1NormReg( vY, paramMu )
%   Solves g \left( x \right) = \mu {\left\| x \right\|}_{1} + {I}_{\left\| x \right\|}_2 \leq 1} \left( x \right)
%   using closed form solution.
% Input:
%   - mA            -   Model Matrix.
%                       Input model matrix.
%                       Structure: Matrix (m x n).
%                       Type: 'Single' / 'Double'.
%                       Range: (-inf, inf).
% Output:
%   - vX            -   Solution Vector.
%                       The solution to the optimization problem..
%                       Structure: Vector (m x 1).
%                       Type: 'Single' / 'Double'.
%                       Range: (-inf, inf).
% References
%   1.  A
% Remarks:
%   1.  B
% TODO:
%   1.  C
% Release Notes:
%   -   1.0.000     20/03/2020  Royi Avital
%       *   First release version.
% ----------------------------------------------------------------------------------------------- %

FALSE   = 0;

    
function [ vX ] = ProxBoxIndicatorLInfNormReg( vY, paramAlpha )
% ----------------------------------------------------------------------------------------------- %
% [ vX ] = ProxL2BallIndicatorL1NormReg( vY, paramMu )
%   Solves g \left( x \right) = \mu {\left\| x \right\|}_{1} + {I}_{\left\| x \right\|}_2 \leq 1} \left( x \right)
%   using closed form solution.
% Input:
%   - mA            -   Model Matrix.
%                       Input model matrix.
%                       Structure: Matrix (m x n).
%                       Type: 'Single' / 'Double'.
%                       Range: (-inf, inf).
% Output:
%   - vX            -   Solution Vector.
%                       The solution to the optimization problem..
%                       Structure: Vector (m x 1).
%                       Type: 'Single' / 'Double'.
%                       Range: (-inf, inf).
% References
%   1.  A
% Remarks:
%   1.  B
% TODO:
%   1.  C
% Release Notes:
%   -   1.0.000     20/03/2020  Royi Avital
%       *   First release version.
% ----------------------------------------------------------------------------------------------- %

FALSE   = 0;
TRUE    = 1;

OFF     = 0;
ON      = 1;

hProxLInf = @(vX, paramLambda) vX - (paramLambda * ProjectL1Ball(vX / paramLambda, 1, 1e-6));

vX1 = min(max(vY, 0), 1);
vX1 = hProxLInf(vX1, paramAlpha);

valX1 = 0.5 * sum((vX1 - vY) .^ 2) + (paramAlpha * max(abs(vX1)));

vX2 = hProxLInf(vY, paramAlpha);
vX2 = min(max(vX2, 0), 1);

valX2 = 0.5 * sum((vX2 - vY) .^ 2) + (paramAlpha * max(abs(vX2)));

if(valX1 < valX2)
    vX = vX1;
else
    vX = vX2;
end