Practical Optimization for Mechanical Engineers

Lecture

Date: Monday, 16:00h - 17:30h

Room: MB 243

Start: 09.10.2023

Exercise/Lab

Date: Wednesday, 10:15h - 12:00h

Room: MB 142

Start: 11.10.2023

Course goals

This course focuses on the basic methods for solving linear and nonlinear constrained optimization problems, including the direct discretization of optimal control problems and simple neural networks, making special emphasis in the educated use of the state-of-the-art routines offered by Matlab’s optimization toolbox. Most of the examples and exercises presented in this course are derived from actual applications in the fields of mechanics and robotics.

The goal of the course is to train the students on how to solve practical optimization problems efficiently using tools like Matlab.

Content

The course is organized in five parts, each part focusing on the understanding of one family of optimization problems. In each part, at least one practical problem will be discussed in detail and subsequently solved using Matlab.

  1. Linear optimization problems: Simplex method
  2. Unconstrained nonlinear problem: Basic descent methods, Newton methods
  3. Constrained nonlinear problems: Penalty and barrier methods, Lagrange methods, Sequential quadratic programming methods
  4. Introduction to neural networks
  5. Introduction to calculus of variations and optimal control

Information regarding the exam

The exam is split into two parts: A theoretical part, in which students solve given optimization problems using pen a paper (2/3 of the final mark), and a practical part in which students solve a given optimization problem using Matlab (1/3 of the final mark). To pass the exam, a minimum of 40% of the points corresponding to each part must be attained.

Further details about the exam will be given at a later point in time.

Administration and downloads

Moodle