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: ProxLogisticLossFunctionGd.m
    path of file: ./repos/StackExchangeCodes/Mathematics/Q1683654
    
    the code of the file until where you have to start completion: function [ vX ] = ProxLogisticLossFunctionGd( vX, vY, vC, paramLambda, numIterations, stopThr )
% ----------------------------------------------------------------------------------------------- %
% [ vX ] = ProxLogisticLossFunction( vX, vY, vC, paramLambda, numIterations, stopThr )
%   Calculates the Prox of the Logistic Cost Function using Gradient Descent.
% Input:
%   - vX            -   Input Vector.
%                       Starting point for the iterative procedure.
%                       Structure: Vector (Column).
%                       Type: 'Single' / 'Double'.
%                       Range: (-inf, inf).
%   - vY            -   Input Vector.
%                       Structure: Vector (Column).
%                       Type: 'Single' / 'Double'.
%                       Range: (-inf, inf).
%   - vC            -   Model Vector.
%                       The model vector in the Logsitic Cost Function.
%                       Structure: Vector (Column).
%                       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            -   Output Vector.
%                       The Prox for the logistic cost function for the
%                       input vector 'vY'.
%                       Structure: Vector (Column).
%                       Type: 'Single' / 'Double'.
%                       Range: (-inf, inf).
% References
%   1.  https://math.stackexchange.com/a/3571521/33.
%   2.  Elementary Numerical Analysis MATH:3800/CS:3700(22M:072/22C:072)
%       (https://homepage.divms.uiowa.edu/~whan/3800.d/3800.html, Section 3.4).
% Remarks:
%   1.  On some cases it fails to converge (While Newton Method converge to
%       the right solution).
% TODO:
%   1.  C
% Release Notes:
%   -   1.0.000     06/03/2020  Royi Avital
%       *   First release version.
% ----------------------------------------------------------------------------------------------- %

FALSE   = 0;
TRUE    = 1;

OFF     = 0;
ON      = 1;

vXPrev = vX;
vG = zeros(size(vX, 1), 1);

hObjFun = @(vX) 0.5 * sum((vX - vY) .^ 2) + (paramLambda * log(1 + exp(-vC.' * vX)));
stepSizeMax = 2;

    
function [ vX ] = ProxLogisticLossFunctionGd( vX, vY, vC, paramLambda, numIterations, stopThr )
% ----------------------------------------------------------------------------------------------- %
% [ vX ] = ProxLogisticLossFunction( vX, vY, vC, paramLambda, numIterations, stopThr )
%   Calculates the Prox of the Logistic Cost Function using Gradient Descent.
% Input:
%   - vX            -   Input Vector.
%                       Starting point for the iterative procedure.
%                       Structure: Vector (Column).
%                       Type: 'Single' / 'Double'.
%                       Range: (-inf, inf).
%   - vY            -   Input Vector.
%                       Structure: Vector (Column).
%                       Type: 'Single' / 'Double'.
%                       Range: (-inf, inf).
%   - vC            -   Model Vector.
%                       The model vector in the Logsitic Cost Function.
%                       Structure: Vector (Column).
%                       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            -   Output Vector.
%                       The Prox for the logistic cost function for the
%                       input vector 'vY'.
%                       Structure: Vector (Column).
%                       Type: 'Single' / 'Double'.
%                       Range: (-inf, inf).
% References
%   1.  https://math.stackexchange.com/a/3571521/33.
%   2.  Elementary Numerical Analysis MATH:3800/CS:3700(22M:072/22C:072)
%       (https://homepage.divms.uiowa.edu/~whan/3800.d/3800.html, Section 3.4).
% Remarks:
%   1.  On some cases it fails to converge (While Newton Method converge to
%       the right solution).
% TODO:
%   1.  C
% Release Notes:
%   -   1.0.000     06/03/2020  Royi Avital
%       *   First release version.
% ----------------------------------------------------------------------------------------------- %

FALSE   = 0;
TRUE    = 1;

OFF     = 0;
ON      = 1;

vXPrev = vX;
vG = zeros(size(vX, 1), 1);

hObjFun = @(vX) 0.5 * sum((vX - vY) .^ 2) + (paramLambda * log(1 + exp(-vC.' * vX)));
stepSizeMax = 2;
sSolverOptions = optimset('fminbnd');
sSolverOptions = optimset(sSolverOptions, 'Display', 'off');

for ii = 1: numIterations
    vXPrev(:) = vX;
    
    valExp  = exp(-vC.' * vX);
    vG(:) = vX - vY - (paramLambda * (valExp / (1 + valExp)) * vC);
    
    hStepSizeFun    = @(stepSize) hObjFun(vX - (stepSize * vG));
    stepSize        = fminbnd(hStepSizeFun, 0, stepSizeMax, sSolverOptions);
    
    vX(:) = vX - (stepSize * vG);
    
    if(max(abs(vX - vXPrev)) < stopThr)
        break;
    end