Book contents
- Frontmatter
- Contents
- Preface
- 1 Introduction and Concepts
- 2 Large Neighborhood Search
- 3 Rounding, Propagation and Diving
- 4 The Feasibility Pump Family
- 5 Pivoting and Line Search Heuristics
- 6 Computational Study
- 7 Primal Heuristics for Mixed-Integer Nonlinear Programming
- 8 Machine Learning for Primal Heuristics
- Appendix Quiz Solutions
- References
- Index
1 - Introduction and Concepts
Published online by Cambridge University Press: 04 April 2025
- Frontmatter
- Contents
- Preface
- 1 Introduction and Concepts
- 2 Large Neighborhood Search
- 3 Rounding, Propagation and Diving
- 4 The Feasibility Pump Family
- 5 Pivoting and Line Search Heuristics
- 6 Computational Study
- 7 Primal Heuristics for Mixed-Integer Nonlinear Programming
- 8 Machine Learning for Primal Heuristics
- Appendix Quiz Solutions
- References
- Index
Summary
This chapter introduces the notation used in the book and discusses the mixed integer programming (MIP) computational framework in which heuristics are developed, used, and evaluated. The chapter starts by formally definining MIP and presenting the basic complete algorithms to solve it. Then, the more important building block concepts at the core of primal heuristics are presented, as well as the way in which they are incorporated in the MIP framework and their impact.
- Type
- Chapter
- Information
- Primal Heuristics in Integer Programming , pp. 1 - 29Publisher: Cambridge University PressPrint publication year: 2025