Book contents
- Frontmatter
- Contents
- Notation
- Preface
- Part I Overview of Optimization:Applications and Problem Formulations
- Part II From General Mathematical Background to General Nonlinear Programming Problems (NLP)
- 2 General Concepts
- 3 Convexity
- 4 Quadratic Functions
- 5 Minimization in One Dimension
- 6 Unconstrained Multivariate Gradient-Based Minimization
- 7 Constrained Nonlinear Programming Problems (NLP)
- 8 Penalty and Barrier Function Methods
- 9 Interior Point Methods (IPM’s):A Detailed Analysis
- Part III Formulation and Solution of Linear Programming (LP) Problems
- Index
3 - Convexity
from Part II - From General Mathematical Background to General Nonlinear Programming Problems (NLP)
Published online by Cambridge University Press: 17 December 2020
- Frontmatter
- Contents
- Notation
- Preface
- Part I Overview of Optimization:Applications and Problem Formulations
- Part II From General Mathematical Background to General Nonlinear Programming Problems (NLP)
- 2 General Concepts
- 3 Convexity
- 4 Quadratic Functions
- 5 Minimization in One Dimension
- 6 Unconstrained Multivariate Gradient-Based Minimization
- 7 Constrained Nonlinear Programming Problems (NLP)
- 8 Penalty and Barrier Function Methods
- 9 Interior Point Methods (IPM’s):A Detailed Analysis
- Part III Formulation and Solution of Linear Programming (LP) Problems
- Index
Summary
Convexity is of paramount importance in optimization theory.This chapter adopts a simple and intuitive description, highlighting the importance of these properties to guarantee global optimality, and paves the way to understanding nonconvex optimization problems in later chapters.
Keywords
- Type
- Chapter
- Information
- Optimization for Chemical and Biochemical EngineeringTheory, Algorithms, Modeling and Applications, pp. 38 - 43Publisher: Cambridge University PressPrint publication year: 2021