Content, progress and pedagogy of the module

Learning objectives

Knowledge

• Must have knowledge about different classes of optimization problems.
• Must have knowledge about objective function, global/local minima, constrained/unconstrained, convex/non-convex functions and sets.
• Must have knowledge about the consequences of dimensionality.
• Must have knowledge about gradient and optimal gradient methods.
• Must have knowledge about Newton and interior-point methods for constrained optimization.
• Must have knowledge about line search methods and stop criteria.
• Must have knowledge about tools for non-linear optimization.
• Must have knowledge about methods for solving combinatorial optimization problems, such as Simulated Annealing (SA), Genetic Algorithms (GA), ant colony optimization, and Integer Linear Programming (ILP).

Skills

• Must be able to identify problem classes.
• Must be able to apply optimization methods in order to design and implement algorithms for continuous and discrete optimization.
• Must be able to evaluate the performance of optimization algorithms.
• Must be able to transform optimization problems to standard form and use off-the-shell optimization software.
• Must be able to evaluate and understand numerical aspects of optimization algorithms.

Competences

• Must have an understanding of how to formulate optimization problems in signal processing.
• Must have competencies in applying optimization in signal processing applications.

Type of instruction

As described in the introduction to Chapter 3.

Exam

Exams