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
7 - Constrained Nonlinear Programming Problems (NLP)
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
This is one of the main and key chapters in the introductory material part of this book.Constrained nonlinear programming, involving both equality and inequality constraints, is introduced and related in an intuitive (at this stage) manner with Lagrange multipliers.In a later chapter (duality theory, Chapter 17) a more rigorous and theoretical introduction to Lagrangian theory is presented.
- Type
- Chapter
- Information
- Optimization for Chemical and Biochemical EngineeringTheory, Algorithms, Modeling and Applications, pp. 81 - 90Publisher: Cambridge University PressPrint publication year: 2021