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demo.m
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demo.m
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function demo()
% demonstration file for SGDLibrary.
%
% This file illustrates how to use this library in case of linear
% regression problem. This demonstrates SGD and SVRG algorithms.
%
% This file is part of SGDLibrary.
%
% Created by H.Kasai on Oct. 24, 2016
% Modified by H.Kasai on Nov. 03, 2016
clc;
clear;
close all;
%% generate synthetic data
% set number of dimensions
d = 3;
% set number of samples
n = 300;
% generate data
data = logistic_regression_data_generator(n, d);
%% define problem definitions
problem = logistic_regression(data.x_train, data.y_train, data.x_test, data.y_test);
%% perform algorithms SGD and SVRG
options.w_init = data.w_init;
options.step_init = 0.01;
options.verbose = 2;
[w_sgd, info_sgd] = sgd(problem, options);
[w_svrg, info_svrg] = svrg(problem, options);
%% display cost/optimality gap vs number of gradient evaluations
display_graph('grad_calc_count','cost', {'SGD', 'SVRG'}, {w_sgd, w_svrg}, {info_sgd, info_svrg});
end