Seminar notes mathematical optimization

Seminar notes mathematical optimization#

Created: 2022-01-11

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”#

mswin_octave_blas

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;
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