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
13 - Regression Revisited: Using LP to Fit Linear Models
from Part III - Formulation and Solution of Linear Programming (LP) Problems
Published online by Cambridge University Press: 17 December 2020
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
Summary
This chapter introduces concepts of norm-1 and infinity norm fitting, both in terms of their own merit as useful fitting techniques, apart from least squares, but also importantly to teach how optimization problems that seem hard to solve (such as by being non-differentiable) can be reformulated effectively into easier ones that can be handled by standard solution methods – in this case by LP solvers.
Keywords
- Type
- Chapter
- Information
- Optimization for Chemical and Biochemical EngineeringTheory, Algorithms, Modeling and Applications, pp. 146 - 150Publisher: Cambridge University PressPrint publication year: 2021