Part 1 · Chapter 4

Optimization Basics

Introduces objectives, constraints, convexity, and numerical algorithms as the mathematical engine behind model training and engineering design decisions.

Chapter map

What this chapter develops

  • Objectives and design variables
  • Unconstrained optimization
  • Constrained optimization
  • Convexity
  • Gradient-based algorithms
  • Practical numerical considerations

Engineering practice

Application and Diamond Examples

  1. 01

    Diamond examples pair engineering optimization problems with machine learning objectives throughout the chapter

Companion materials

Portal resources

Chapter catalogFind this chapter’s indexed examples and related topics.
Python notebooksNo companion AE notebooks are currently assigned to this chapter.None
AE datasetsNo companion AE datasets are currently assigned to this chapter.None
DiscussionAsk chapter-specific questions and compare engineering interpretations with other readers.