Today I completed my seminar notes about selected topics in mathematical optimization: optimality conditions for finite-dimensional (un-)constrained continuous problems, (un-)constrained optimization methods, convex analysis, and many GNU Octave / Matlab examples. The material was created with Jupyter Book and JupyterLab running the octave_kernel using the Octave Docker image.
Seminar “Selected Topics in Mathematical Optimization”
N = 3;
[X,Y] = meshgrid (linspace (-N, N, 40));
% Gaussian probability density function (PDF)
GAUSS = @(sigma, mu) 1 / (sigma * sqrt (2*pi)) * ...
exp (-0.5 * ((X - mu(1)).^2 + (Y - mu (2)).^2) / sigma^2);
Z = 9 * GAUSS (0.6, [ 0.0, 2.0]) + 5 * GAUSS (0.5, [ 1.0, 0.0]) ...
+ 3 * GAUSS (0.4, [-0.5, 0.0]) - 3 * GAUSS (0.3, [-1.5, 0.5]) ...
- 7 * GAUSS (0.5, [ 0.0, -2.0]);
surf (X, Y, Z);
colormap ('jet');
view (-55, 21);
axis off;