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Conducting an Eldo Optimization
Optimization
Conducting an Eldo Optimization
This section discusses specific types of optimization problem and explains the options available
to influence the behavior of the optimizer in each of these cases. It covers:
Global and Local Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 629
Continuous and Discrete Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 630
Smooth and Non-Smooth Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 631
Multiple-Run Compatibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 632
Optimization of Sweep Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 633
Global and Local Optimization
The fastest optimization algorithms only search for a local solution at a point at which the
objective function is smaller than all other feasible points in its vicinity. They do not always
find the best of all such minima, that is the global solution. Practical methods for finding global
optima are currently too expensive in all but the most specialized cases.
Figure 13-6 shows local and global solutions.
Figure 13-6. Different Types of Minima
General non-linear problems may possess local solutions that are not global solutions. Global
solutions are highly desirable, however, they are usually difficult to identify and even more
difficult to locate. Unless very strong assumptions are made about the functions that define the
optimization problem in question, characterizations of global minima are almost impossible.
Note
The Eldo Optimizer SQP Algorithm is based on Newton iterations. It is not able to combine
local and global searches, and only finds local solutions.
Related Topics
Design Variables
Design Objectives
Eldo® User's Manual, 15.3 629