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: SolveLsBoxConstraints.m
    path of file: ./repos/StackExchangeCodes/Mathematics/Q3558240
    
    the code of the file until where you have to start completion: function [ vX, mX ] = SolveLsBoxConstraints( mA, vB, vC, vD, vX, numIterations, stopTol )
% ----------------------------------------------------------------------------------------------- %
% [ vX, mX ] = SolveLsBoxConstraints( mA, vB, vC, vD, vX, numIterations, stopTol )
%   Solves \arg \min_{x} 0.5 || A x - b ||, s.t. c <= x <= d using
%   Gradient Descent Method.
% Input:
%   - mA            -   Model Matrix.
%                       Input model matrix.
%                       Structure: Matrix (m x n).
%                       Type: 'Single' / 'Double'.
%                       Range: (-inf, inf).
%   - vB            -   Measurements Vector.
%                       Given data vector.
%                       Structure: Vector (n x 1).
%                       Type: 'Single' / 'Double'.
%                       Range: (-inf, inf).
%   - vC            -   Box Constraints Vector (Minimum Value).
%                       Sets the minimum value for the solution.
%                       Structure: Vector (m x 1).
%                       Type: 'Single' / 'Double'.
%                       Range: (-inf, inf).
%   - vD            -   Box Constraints Vector (Maximum Value).
%                       Sets the maximum value for the solution.
%                       Structure: Vector (m x 1).
%                       Type: 'Single' / 'Double'.
%                       Range: (-inf, inf).
%   - numIterations -   Number of Iterations.
%                       Sets the number of iterations for the algorithm to
%                       run.
%                       Structure: Scalar.
%                       Type: 'Single' / 'Double'.
%                       Range {1, 2, ...}.
%   - stopTol       -   Stopping Condition Tolerance.
%                       Sets the stopping threshold for the L Inf (Maximum
%                       Absolute Value) of the change between 2 iterations
%                       of the algorithm.
%                       Structure: Scalar.
%                       Type: 'Single' / 'Double'.
%                       Range [0, inf).
% Output:
%   - vX            -   Solution Vector.
%                       The solution to the optimization problem..
%                       Structure: Vector (m x 1).
%                       Type: 'Single' / 'Double'.

    
function [ vX, mX ] = SolveLsBoxConstraints( mA, vB, vC, vD, vX, numIterations, stopTol )
% ----------------------------------------------------------------------------------------------- %
% [ vX, mX ] = SolveLsBoxConstraints( mA, vB, vC, vD, vX, numIterations, stopTol )
%   Solves \arg \min_{x} 0.5 || A x - b ||, s.t. c <= x <= d using
%   Gradient Descent Method.
% Input:
%   - mA            -   Model Matrix.
%                       Input model matrix.
%                       Structure: Matrix (m x n).
%                       Type: 'Single' / 'Double'.
%                       Range: (-inf, inf).
%   - vB            -   Measurements Vector.
%                       Given data vector.
%                       Structure: Vector (n x 1).
%                       Type: 'Single' / 'Double'.
%                       Range: (-inf, inf).
%   - vC            -   Box Constraints Vector (Minimum Value).
%                       Sets the minimum value for the solution.
%                       Structure: Vector (m x 1).
%                       Type: 'Single' / 'Double'.
%                       Range: (-inf, inf).
%   - vD            -   Box Constraints Vector (Maximum Value).
%                       Sets the maximum value for the solution.
%                       Structure: Vector (m x 1).
%                       Type: 'Single' / 'Double'.
%                       Range: (-inf, inf).
%   - numIterations -   Number of Iterations.
%                       Sets the number of iterations for the algorithm to
%                       run.
%                       Structure: Scalar.
%                       Type: 'Single' / 'Double'.
%                       Range {1, 2, ...}.
%   - stopTol       -   Stopping Condition Tolerance.
%                       Sets the stopping threshold for the L Inf (Maximum
%                       Absolute Value) of the change between 2 iterations
%                       of the algorithm.
%                       Structure: Scalar.
%                       Type: 'Single' / 'Double'.
%                       Range [0, inf).
% Output:
%   - vX            -   Solution Vector.
%                       The solution to the optimization problem..
%                       Structure: Vector (m x 1).
%                       Type: 'Single' / 'Double'.
%                       Range: (-inf, inf).
%   - mX            -   Solution Path Matrix.
%                       Matrix which each of its columns embeds the
%                       solution of the i-th step.
%                       Structure: Matrix (m x numIterations).
%                       Type: 'Single' / 'Double'.
%                       Range: (-inf, inf).
% References
%   1.  A
% Remarks:
%   1.  If one sets 'stopTol = 0' then the algorithm will run the given
%       number of iterations.
% TODO:
%   1.  C
% Release Notes:
%   -   1.0.000     24/02/2020  Royi Avital
%       *   First release version.
% ----------------------------------------------------------------------------------------------- %

FALSE   = 0;
TRUE    = 1;

OFF     = 0;
ON      = 1;

% vX = mA \ vB;
% 
% if(all(vX >= vC) && all(vX <= vD))
%     return;
% end