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
8 - Machine Learning for Primal Heuristics
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
The integration of machine learning models within MIP computation has been an exciting research trend in the last decade. This chapter reviews the use of such models in conjunction with primal heuristics for MIP.
- Type
- Chapter
- Information
- Primal Heuristics in Integer Programming , pp. 103 - 112Publisher: Cambridge University PressPrint publication year: 2025