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)
- Part III Formulation and Solution of Linear Programming (LP) Problems
- 10 Introduction to LP Models
- 11 Numerical Solution of LP Problems Using the Simplex Method
- 12 A Sampler of LP Problem Formulations
- 13 Regression Revisited: Using LP to Fit Linear Models
- 14 Network Flow Problems
- 15 LP and Sensitivity Analysis, in Brief
- 16 Multiobjective Optimization
- 17 Optimization under Uncertainty
- 18 Mixed-Integer Programming Problems
- 19 Global Optimization
- 20 Optimal Control Problems (Dynamic Optimization)
- 21 System Identification and Model Predictive Control
- Index
18 - Mixed-Integer Programming Problems
from Part III - Formulation and Solution of Linear Programming (LP) Problems
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)
- Part III Formulation and Solution of Linear Programming (LP) Problems
- 10 Introduction to LP Models
- 11 Numerical Solution of LP Problems Using the Simplex Method
- 12 A Sampler of LP Problem Formulations
- 13 Regression Revisited: Using LP to Fit Linear Models
- 14 Network Flow Problems
- 15 LP and Sensitivity Analysis, in Brief
- 16 Multiobjective Optimization
- 17 Optimization under Uncertainty
- 18 Mixed-Integer Programming Problems
- 19 Global Optimization
- 20 Optimal Control Problems (Dynamic Optimization)
- 21 System Identification and Model Predictive Control
- Index
Summary
Lagrangian relaxation and Lagrangian decomposition are key approaches to reducing the complexity of classes of optimization problems so as to facilitate their solution.Key concepts and applications are presented in this chapter.
- Type
- Chapter
- Information
- Optimization for Chemical and Biochemical EngineeringTheory, Algorithms, Modeling and Applications, pp. 228 - 260Publisher: Cambridge University PressPrint publication year: 2021