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: D_SGD_stronglyconvex.m
    path of file: ./repos/Performance-Estimation-Toolbox/Examples/04_Stochastic methods for convex minimization
    
    the code of the file until where you have to start completion: function D_SGD_stronglyconvex
% In this example, we use stochastic gradient descent for solving the
% finite sum minimization problem
%   min_x {F(x)= 1/n [f1(x)+ ... + fn(x)]  }
% for notational convenience we denote xs=argmin_x F(x).
% Functions f1,...,fn are assumed L-smooth and mu-strongly convex.
%
% In addition, we assume a bounded variance at optimality:
%           E||fi'(x*)||^2 <= v^2,
% which is standard from the SGD literature.
%
% This code compute the exact worst-case guarantee for the distance to
% optimality. We will observe it does not depend on n for this particular
% setting, meaning that the guarantees are also valid for expectation
% minimization settings (i.e., when n goes to infinity).
% 

% Initialize an empty PEP
P = pep();


    
function D_SGD_stronglyconvex
% In this example, we use stochastic gradient descent for solving the
% finite sum minimization problem
%   min_x {F(x)= 1/n [f1(x)+ ... + fn(x)]  }
% for notational convenience we denote xs=argmin_x F(x).
% Functions f1,...,fn are assumed L-smooth and mu-strongly convex.
%
% In addition, we assume a bounded variance at optimality:
%           E||fi'(x*)||^2 <= v^2,
% which is standard from the SGD literature.
%
% This code compute the exact worst-case guarantee for the distance to
% optimality. We will observe it does not depend on n for this particular
% setting, meaning that the guarantees are also valid for expectation
% minimization settings (i.e., when n goes to infinity).
% 

% Initialize an empty PEP
P = pep();

% Problem setup:
L       = 1;        % Smoothness
m       = .1;       % Strong convexity
n       = 5;        % Number of functions in the finite sum
v       = 1;        % variance: E[||fi'(x*)||^2] <= v^2
gamma   = 1/L;      % stepsize
R       = 2;        % initial ||x0-xs||^2
kappa   = L/m;
% (1) Set up the objective function
param.L   = L;
param.mu  = m;

f{1} = P.DeclareFunction('SmoothStronglyConvex',param);
F = f{1}/n;              % F is the objective function
for i = 2:n
    f{i} = P.DeclareFunction('SmoothStronglyConvex',param);
    F    = F + f{i}/n;
end