Content, progress and pedagogy of the
- Must have knowledge about different classes of optimization
- Must have knowledge about objective function, global/local
minima, constrained/unconstrained, convex/non-convex functions and
- Must have knowledge about the consequences of
- Must have knowledge about gradient and optimal gradient
- Must have knowledge about Newton and interior-point methods for
- Must have knowledge about line search methods and stop
- Must have knowledge about tools for non-linear
- Must have knowledge about methods for solving combinatorial
optimization problems, such as Simulated Annealing (SA), Genetic
Algorithms (GA), ant colony optimization, and Integer Linear
- 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
- Must be able to evaluate the performance of optimization
- 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
- Must have an understanding of how to formulate optimization
problems in signal processing.
- Must have competencies in applying optimization in signal
Type of instruction
As described in the introduction to Chapter 3.